BIOVISION

BIOVISION - 2025

2025Activity report‌Project-TeamBIOVISION

RNSR: 201622040S‌

Research center Inria Centre‌‌ at Université Côte d'Azur‌
Team name: Biologically plausible Integrative mOdels of the‌ Visual system : towards synergIstic Solutions for visually-Impaired‌ people and artificial visiON

Creation of the Project-Team:‌ 2018 August 01

Each year, Inria research teams‌ publish an Activity Report presenting their work and‌ results over the reporting period. These reports follow‌ a common structure, with some optional sections depending‌ on the specific team. They typically begin by‌ outlining the overall objectives and research programme, including‌ the main research themes, goals, and methodological approaches.‌ They also describe the application domains targeted by‌ the team, highlighting the scientific or societal contexts‌ in which their work is situated.

The reports‌ then present the highlights of the year, covering‌ major scientific achievements, software developments, or teaching contributions.‌ When relevant, they include sections on software, platforms,‌ and open data, detailing the tools developed and‌ how they are shared. A substantial part is‌ dedicated to new results, where scientific contributions are‌ described in detail, often with subsections specifying participants‌ and associated keywords.

Finally, the Activity Report addresses‌ funding, contracts, partnerships, and collaborations at various levels,‌ from industrial agreements to international cooperations. It also‌ covers dissemination and teaching activities, such as participation‌ in scientific events, outreach, and supervision. The document‌ concludes with a presentation of scientific production, including‌ major publications and those produced during the year.‌

Keywords

Computer Science and Digital Science

A5.1.1. Engineering‌ of interactive systems
A5.1.2. Evaluation of interactive systems‌
A5.1.9. User and perceptual studies
A5.3. Image processing‌ and analysis
A5.5.4. Animation
A5.6.1. Virtual reality
A5.6.2.‌ Augmented reality
A5.8. Natural language processing
A6.1.1. Continuous‌ Modeling (PDE, ODE)
A6.1.4. Multiscale modeling
A6.1.5. Multiphysics‌ modeling
A6.2.4. Statistical methods
A6.3.3. Data processing
A7.1.3.‌ Graph algorithms
A9.4. Natural language processing
A9.7. AI‌ algorithmics
A9.12. Computer vision

1 Team‌ members, visitors, external collaborators

Research Scientists

Bruno Cessac‌ [Team leader, INRIA, Senior Researcher‌, HDR]
Pierre Kornprobst [INRIA,‌ Senior Researcher, HDR]
Hui-Yin Wu [‌INRIA, ISFP]

Post-Doctoral Fellow

Paritosh Sharma‌ [INRIA, Post-Doctoral Fellow]

PhD Students‌

Johanna Delachambre [UNIV COTE D'AZUR]
Pauline‌ Devictor [UNIV COTE D'AZUR]
Franz Franco‌ Gallo [UNIV COTE D'AZUR, ATER,‌ from Sep 2025]
Franz Franco Gallo [‌INRIA, until Aug 2025]
Sebastian Gallardo‌ Diaz [Demain un Autre Jour, CIFRE‌]
Erwan Petit [UNIV COTE D'AZUR]‌
Laura Piovano [UNIV COTE D'AZUR, from‌ Oct 2025]

Technical Staff

Jerome Emonet [‌INRIA, Engineer, from Feb 2025 until‌ Apr 2025]

Interns and Apprentices

Gregoire Arrabie-Aubies [INRIA, Intern‌, from Jun 2025‌ until Aug 2025]‌‌
Andres Navarro Galleguillos [INRIA, Intern,‌ from Feb 2025 until‌ Apr 2025]
Wei‌‌ Tung Pan [INRIA, Intern, until‌ Jun 2025]
Laura‌ Piovano [UNIV COTE‌‌ D'AZUR, Intern, until Feb 2025]‌
Mranmay Shetty [UNIV‌ COTE D'AZUR, Intern‌‌, from Nov 2025]
Mranmay Shetty [‌UNIV COTE D'AZUR,‌ Intern, from Mar‌‌ 2025 until May 2025]

Administrative Assistant

Marie-Cecile‌ Lafont [INRIA]‌

External Collaborators

Aurélie Calabrese‌‌ [CNRS]
Eric Castet [CNRS]‌
Florent Robert [POLE‌ EMPLOI, from Feb‌‌ 2025 until Aug 2025]

2 Overall objectives‌

Vision is a key‌ function to sense our‌‌ world and perform complex tasks. It has high‌ sensitivity and strong reliability,‌ even though most of‌‌ its input is noisy, changing, and ambiguous. A‌ better understanding of how‌ biological vision works opens‌‌ up scientific challenges as well as promising technological,‌ medical and societal breakthroughs.‌ Fundamental aspects such as‌‌ understanding how a visual scene is encoded by‌ the retina into spike‌ trains, transmitted to the‌‌ visual cortex via the optic nerve through the‌ thalamus, decoded in a‌ fast and efficient way,‌‌ and then creating a sense of perception, offers‌ perspectives in research and‌ technological developments for current‌‌ and future generations.

Vision is not always functional‌ though. Sometimes, "something" goes‌ wrong. Although many visual‌‌ impairments such as myopia, hypermetropia, cataract, can be‌ cured by glasses, contact‌ lenses, or other means‌‌ like medicine or surgery, pathologies impairing the retina‌ such as Age-Related Macular‌ Degeneration (AMD) and Retinis‌‌ Pigmentosa (RP) can not be fixed with these‌ standard treatments 44.‌ They result in a‌‌ progressive degradation of vision (Figure 1), up‌ to a stage of‌ low vision (visual acuity‌‌ of less than 6/18 to light perception, or‌ a visual field of‌ less than 10 degrees‌‌ from the point of fixation) or blindness. Thus,‌ people with low vision‌ must learn to adjust‌‌ to their pathologies. Progress in research and technology‌ can help them. Considering‌ the aging of the‌‌ population in developed countries and its strong correlation‌ with the prevalence of‌ eye diseases, low vision‌‌ has already become a major societal problem.

Figure‌ 1:
Central blind‌‌ spot (i.e., scotoma), as perceived by an individual‌ suffering from Central Field‌ Loss (CFL) when looking‌‌ at someone's face.

In this context, the Biovision‌ Team's research revolves around‌ the central theme biological‌‌ vision and perception, and the impact of low‌ vision conditions. Our strategy‌ is based upon four‌‌ cornerstones: To model, to assist diagnosis, to aid‌ visual activities like reading,‌ and to enable personalized‌‌ content creation. We aim to develop fundamental research‌ as well as technology‌ transfer along three entangled‌‌ axes of research:

Axis‌ 1: Modeling the retina and the primary visual‌ system.
Axis 2: Diagnosis, rehabilitation, and low-vision aids.‌
Axis 3: Visual media analysis and creation.

These‌ axes form a stable, three-pillared basis for our‌ research activities, giving our team an original combination‌ in expertise: modeling for the neurosciences, computer vision,‌ Virtual and Augmented Reality (XR), and media analysis‌ and creation. Our research themes require strong interactions‌ with experimental neuroscientists, modelers, ophtalmologists and patients, constituting‌ a large network of national and international collaborators.‌ Biovision is therefore a strongly multi-disciplinary team. We‌ publish in international reviews and conferences in several‌ fields including neuroscience, low vision, mathematics, physics, computer‌ vision, multimedia, computer graphics, and human-computer interactions.

3‌ Research program

3.1 Axis 1 - Modeling the‌ retina and the primary visual system.

In collaboration‌ with neuroscience labs, we derive phenomenological equations and‌ analyze them mathematically by adopting methods from theoretical‌ physics or mathematics (Figure 2). We also‌ develop simulation platforms like Pranas or Macular,‌ helping us confront theoretical predictions to numerical simulations,‌ or allowing researchers to perform in silico experimentation‌ under conditions rarely accessible to experimentalists (such as‌ simultaneously recording the retina layers and the primary‌ visual cortex1 (V1)). Specifically, our research focuses‌ on the modeling and mathematical study of:

Multi-scale‌ dynamics of the retina in the presence of‌ spatio-temporal stimuli;
Response to motion, anticipation and surprise‌ in the early visual system;
Spatio-temporal coding and‌ decoding of visual scenes by spike trains;
Retinal‌ pathologies.

Figure 2:‌
The process of retina modeling. A) The retina‌ structure from biology. B) Designing a simplified architecture‌ keeping retina components that we want to better‌ understand (here, the role of Amacrine cells in‌ motion anticipation); C) Deriving mathematical equations from A‌ and B. D, E). Results from numerical modeling‌ and mathematical modeling. Here, our retina model's response‌ to a parabolic motion where the peak of‌ response resulting from amacrine cells connectivity or gain‌ control is in advance with respect to the‌ peak in response without these mechanisms. This illustrates‌ retinal anticipation.

3.2 Axis 2 - Diagnosis, rehabilitation,‌ and low-vision aids.

In collaboration with low vision‌ clinical centers and cognitive science labs, we develop‌ computer science methods, open software and toolboxes to‌ assist low vision patients (Figure 3), with‌ a particular focus on Age-Related Macular Degeneration2‌. As AMD patients still have a plastic and functional vision in‌ their peripheral visual field‌ 49, they must‌‌ develop efficient “Eccentric Viewing" (EV) to adapt to‌ the central blind zone‌ (scotoma) and to direct‌‌ gaze away from the object they want to‌ identify 54. Commonly‌ proposed assistance tools involve‌‌ visual rehabilitation methods 50 and visual aids that‌ usually consist of magnifiers‌ 45.

Our main‌‌ research goals are:

Understanding the relations between anatomo-functional‌ and behavioral observations;
Diagnosis‌ from reading performance screening‌‌ and oculomotor behavior analysis;
Personalized and gamified rehabilitation‌ for training eccentric viewing‌ in VR (Virtual Reality);‌‌
Personalized visual aid systems for daily living activities.‌

Figure 3‌:
Multiple methodologies in‌ graphics and image processing‌‌ have applications towards low-vision technologies including (a) 3D‌ virtual environments for studies‌ of user perception and‌‌ behavior, and creating 3D stimuli for model testing,‌ (b) image enhancement techniques‌ to magnify and increase‌‌ visibility of contours of objects and people, and‌ (c) personalization of media‌ content such as text‌‌ in 360 degrees visual space using VR headsets.‌

3.3 Axis 3 -‌ Visual media analysis and‌‌ creation.

We investigate the impact of visual media‌ design on user experience‌ and perception, and propose‌‌ assisted creativity tools for creating personalized and adapted‌ media content (Figure 4‌). We employ computer‌‌ vision and deep learning techniques for media understanding‌ in film and in‌ complex documents like newspapers.‌‌ We deploy this understanding in new media platforms‌ such as virtual and‌ augmented reality for applications‌‌ in low-vision training, accessible media design, and generation‌ of 3D visual stimuli:‌

Accessible / personalized media‌‌ design for low vision training and reading platforms;‌
Assisted creativity tools for‌ 3D environments;
Visual system‌‌ and multivariate user behavior modeling in 3D contextual‌ environments;
Visual media understanding‌ and gender representation in‌‌ film.

Figure 4: We‌ try to understand the‌ perception of users in‌‌ face of media and the gaps therein –‌ on ontology, intersubjectivity, and‌ intentionality – that result‌‌ from digital interfaces that mediate the digital content‌ and the communication between‌ content designers and end‌‌ users.

4 Application domains‌

4.1 Applications of low-vision studies

Cognitive research. Virtual‌ reality technology represents a new opportunity to conduct‌ cognitive and behavioral research in virtual environments where‌ all parameters can be psychophysically controlled. In the‌ scope of ANR DEVISE, we are currently developing‌ and using the PTVR software (Perception Toolbox for‌ Virtual Reality) 1 to make our own experimental‌ protocols to study low vision. However, we believe‌ that the potential of PTVR is much larger‌ as it could be useful to any researcher‌ familiar with Python programming willing to create and‌ analyze a sophisticated experiment in VR with parsimonious‌ code.
Serious games. Serious games use game mechanics‌ in order to achieve goals such as in‌ training, education, or awareness. In our context, we‌ want to explore serious games as a way‌ to help low-vision patients in performing rehabilitation exercises.‌ Virtual and augmented reality technology is a promising‌ platform to develop such rehabilitation exercises targeted to‌ specific pathologies due to their potential to create‌ fully immersive environments, or inject additional information in‌ the real world. For example, with Age-Related Macular‌ Degeneration (AMD), our objective is to propose solutions‌ allowing rehabilitation of visuo-perceptual-motor functions to optimally use‌ residual portions of the peripheral retina defined from‌ anatomo-functional exams 47.
Vision aid-systems. A variety‌ of aids for low-vision people are already on‌ the market. They use various kinds of desktop‌ (e.g., CCTVs), handheld (mobile applications), or wearable (e.g.‌ OxSight, Helios) technologies, and offer different functionalities including‌ magnification, image enhancement, text to speech, face and‌ object recognition. Our goal is to design new‌ solutions allowing autonomous interaction primarily using mixed reality‌ – virtual and augmented reality. This technology could‌ offer new affordable solutions developed in synergy with‌ rehabilitation protocols to provide personalized adaptations and guidance.‌
User understanding. The design of ecological environments using‌ immersive XR technologies can allow the capture and‌ modeling of user behaviors in the context of‌ everyday scenarios. This can allow the building of‌ datasets that can support exploratory analysis 21 and‌ also models of human behavior prediction that include‌ diverse user profiles 7, 6.

4.2‌ Applications of vision modeling studies

Neuroscience research. Making‌ in-silico experiments is a way to reduce the‌ experimental costs, to test hypotheses and design models,‌ and to test algorithms. Our goal is to‌ develop a large-scale simulations platform of the normal‌ and impaired retinas. This platform, called Macular,‌ allows one to test hypotheses on the retina‌ functions in normal vision (such as the role‌ of amacrine cells in motion anticipation 57,‌ or the expected effects of pharmacology on retina‌ dynamics 48). It is also used to‌ mimic specific degeneracies or pharmacologically induced impairments 53‌, as well as to emulate electric stimulation‌ by prostheses. Finally, it allows to feature the‌ retino-cortical (V1) pathway following 20. Thus, the‌ platform provides a realistic entry to models or‌ simulators of the thalamus or the visual cortex, in contrast to the‌ entries usually considered in‌ modeling studies. A paper‌‌ presenting Macular has been published here 19.‌ See also the online‌ documentation.
Education. Macular‌‌ is also targeted as a useful tool for‌ educational purposes, illustrating for‌ students how the retina‌‌ works and responds to visual stimuli.

4.3 Applications‌ of visual media analysis‌ and creation

Engineering immersive‌‌ interactive storytelling platforms. With the sharp rise in‌ popularity of immersive virtual‌ and augmented reality technologies,‌‌ it is important to acknowledge their strong potential‌ to create engaging, embodied‌ experiences. Reaching this goal‌‌ involves investigating the impact of virtual and augmented‌ reality platforms from an‌ interactivity and immersivity perspective:‌‌ how do people parse visual information, react, and‌ respond in the face‌ of content, in the‌‌ face of media 18, 13, 14‌.
Personalized content creation‌ and assisted creativity. Models‌‌ of user perception can be integrated in tools‌ for content creators, such‌ as to simulate low-vision‌‌ conditions to aid in the design of accessible‌ spaces and media, and‌ to diversify immersive scenarios‌‌ used for studying user perception, for entertainment, and‌ for rehabilitation 17,‌ 59.
Media studies‌‌ and social awareness. Investigating interpretable models of media‌ analysis will allow us‌ to provide tools to‌‌ conduct qualitative media studies on large amounts of‌ data in relation to‌ existing societal challenges and‌‌ issues. This includes public outreach on the impact‌ of low-vision on everyday‌ activities through the use‌‌ of immersive media 3, raising awareness towards‌ biases in media 60‌, and developing media‌‌ analysis tools from deep learning paradigms 61.‌

5 Social and environmental‌ responsibility

The research themes‌‌ in the Biovision team have direct social impacts‌ on two fronts:

Low‌ vision: we work‌‌ in partnership with neuroscientists and ophthalmologists to design‌ technologies for the diagnosis‌ and rehabilitation of low-vision‌‌ pathologies, addressing a strong societal challenge.
Accessibility:‌ in concert with researchers‌ in media studies, we‌‌ tackle the social challenge of designing accessible media,‌ including for the population‌ with visual impairments, as‌‌ well as to address media bias both in‌ content design and in‌ machine learning approaches.

6‌‌ Highlights of the year

6.1 Awards

Florent Robert‌ - PhD student under‌ the supervision of Hui-Yin‌‌ Wu, Marco Winckler and Lucile Sassatelli - obtained‌ the EDSTIC best thesis‌ award.

7 Latest software‌‌ developments, platforms, open data

7.1 Latest software developments‌

7.1.1 Macular

Name:
Numerical‌ platform for simulations of‌‌ the primary visual system in normal and pathological‌ conditions
Keywords:
Retina, Vision,‌ Neurosciences
Scientific Description:

Macular‌‌ is built around a central idea: its use‌ and its graphical interface‌ can evolve according to‌‌ the user's objectives. It can therefore be used‌ in user-designed scenarios, such‌ as simulation of retinal‌‌ waves, simulation of retinal and cortical responses to‌ prosthetic stimulation, study of‌ pharmacological impact on retinal‌‌ response, etc. The user can design their own‌ scenarios using the Macular‌ Template Engine.

At the‌‌ heart of Macular is‌ an object called "Cell". Basically these "cells" are‌ inspired by biological cells, but it's more general‌ than that. It can also be a group‌ of cells of the same type, a field‌ generated by a large number of cells (for‌ example a cortical column), or an electrode in‌ a retinal prosthesis. A cell is defined by‌ internal variables (evolving over time), internal parameters (adjusted‌ by cursors), a dynamic evolution (described by a‌ set of differential equations) and inputs. Inputs can‌ come from an external visual scene or from‌ other synaptically connected cells. Synapses are also Macular‌ objects defined by specific variables, parameters, and equations.‌ Cells of the same type are connected in‌ layers according to a graph with a specific‌ type of synapses (intra-layer connectivity). Cells of a‌ different type can also be connected via synapses‌ (inter-layer connectivity).

All the information concerning the types‌ of cells, their inputs, their synapses and the‌ organization of the layers are stored in a‌ file of type .mac (for "macular") defining what‌ we call a "scenario". Different types of scenarios‌ are offered to the user, which they can‌ load and play, while modifying the parameters and‌ viewing the variables (see technical section).
Functional Description:‌
Macular is a simulation platform for the retina‌ and the primary visual cortex, designed to reproduce‌ the response to visual stimuli or to electrical‌ stimuli, in normal vision conditions, or altered (pharmacology,‌ pathology, development).
Release Contributions:
First release.
News of‌ the Year:
We have written a paper to‌ present Macular to the community of computational neuroscience‌ https://inria.hal.science/hal-05312447v2 This paper has been accepted for publication‌ in "Frontiers in Neuroonformatics". In addition, the licence‌ of Macular has been achieved so that Macular‌ is now freely available for the community.
URL:‌
https://team.inria.fr/biovision/macular-software/
Contact:
Bruno Cessac
Participants:
Bruno Cessac, Evgenia‌ Kartsaki, Selma Souihel, Jerome Emonet, Erwan Demairy, Thibaud‌ Kloczko, Come Le Breton, Nicolas Niclausse, Jean-Luc Szpyrka,‌ Julien Wintz

7.1.2 InREAD

Name:
A Next-Generation Platform‌ for Measuring Reading Performance
Keywords:
Diagnostics, Low vision‌
Scientific Description:
InREAD is a software platform designed‌ for the measurement and analysis of reading performance,‌ developed to overcome methodological and technical limitations of‌ existing tools, particularly the MNREAD test. Built as‌ a multiplatform system with native integration of the‌ mnreadR analysis library, InREAD enables standardized administration of‌ reading tests based on calibrated text stimuli, as‌ well as automated extraction of classical psychophysical parameters‌ of reading (maximum reading speed, reading acuity, critical‌ print size, etc.). The scientific goal of InREAD‌ is twofold: (1) to provide a robust tool‌ for collecting high-quality behavioral data on reading in‌ both normal and impaired vision, and (2) to‌ offer an extensible platform for investigating new experimental‌ approaches, including expanded sentence corpora, low-cost oculomotor measurement,‌ and fully automated test administration. Within ongoing clinical‌ collaborations, InREAD aims to improve measurement reproducibility, reduce‌ inter-examiner variability, and enable large-scale standardized data acquisition—including‌ home-based assessment. This technological foundation opens the door to a more comprehensive‌ understanding of reading strategies‌ and supports new research‌‌ paradigms in low-vision science.
Functional Description:
InREAD is‌ a multiplatform software tool‌ designed to easily administer‌‌ a standardized reading test inspired by the widely‌ used MNREAD protocol for‌ assessing reading performance. It‌‌ provides a simple interface for presenting calibrated text,‌ recording reading times, and‌ automatically generating analyses and‌‌ visual summaries powered by the mnreadR library. InREAD‌ thus offers a solid‌ foundational platform, designed to‌‌ be progressively extended with advanced features such as‌ automatic error detection, gaze‌ tracking, and control of‌‌ viewing conditions.
Release Contributions:
The MVP release (v0.1.0)‌ of InREAD represents the‌ first fully usable version‌‌ of the software. It includes operator and patient‌ management, test session history‌ tracking, administration of MNREAD-inspired‌‌ reading tests, and the ability to record and‌ store ETDRS test results.‌
News of the Year:‌‌
Finalization of the MVP for the near-vision testing‌ component, New GUI enabling‌ operator and patient management,‌‌ as well as test session history, Addition of‌ the distance-vision testing component‌ (results recording)
Contact:
Pierre‌‌ Kornprobst
Participants:
Pierre Kornprobst, Aurélie Calabrese
Partner:
CHU‌ Pasteur

7.1.3 GUsT-3D

Name:‌
Guided User Tasks Unity‌‌ plugin for 3D virtual reality environments
Keywords:
3D,‌ Virtual reality, Interactive Scenarios,‌ Ontologies, User study
Functional‌‌ Description:

We present the GUsT-3D framework for designing‌ Guided User Tasks in‌ embodied VR experiences, i.e.,‌‌ tasks that require the user to carry out‌ a series of interactions‌ guided by the constraints‌‌ of the 3D scene. GUsT-3D is implemented as‌ a set of tools‌ that support a 4-step‌‌ workflow to : (1) annotate entities in the‌ scene with names, navigation,‌ and interaction possibilities, (2)‌‌ define user tasks with interactive and timing constraints,‌ (3) manage scene changes,‌ task progress, and user‌‌ behavior logging in real-time, and (4) conduct post-scenario‌ analysis through spatio-temporal queries‌ on user logs, and‌‌ visualizing scene entity relations through a scene graph.‌

The software also includes‌ a set of tools‌‌ for processing gaze tracking data, including: cleaning and‌ synchronization of the data,‌ calculation of fixations with‌‌ I-VT, I-DT, IDTVR, IS5T, Remodnav, and IDVT algorithms,‌ and visualization of the‌ data (points of regard‌‌ and fixations) in both real time and collectively.‌
News of the Year:‌
A new version of‌‌ the software has been released with additional functionalities‌ for collection, processing, and‌ analysis of gaze tracking.‌‌ We also made major changes to the overall‌ workflow (system logging, interactions,‌ bug fixes, etc.). This‌‌ version was used in two user studies.
URL:‌
https://project.inria.fr/creattive3d/gust-3d/
Publications:
hal-04102737,‌ hal-04446066, hal-03635452
Contact:‌‌
Hui-Yin Wu
Participants:
Hui-Yin Wu, Marco Alba Winckler,‌ Lucile Sassatelli, Florent Robert‌
Partner:
I3S

7.1.4 PTVR‌‌

Name:
Perception Toolbox for Virtual Reality: The Open-Source‌ Python Library for Virtual‌ Reality Experiments
Keywords:
Visual‌‌ perception, Behavioral science, Virtual reality
Scientific Description:
PTVR‌ is a virtual reality‌ software environment designed for‌‌ the design and execution of experiments on visual‌ perception. It enables precise‌ control of visual stimuli‌‌ within interactive and realistic‌ 3D scenes, fine synchronization with eye-tracking systems, and‌ collection of behavioral and physiological measurements. PTVR facilitates‌ the study of complex perceptual mechanisms, such as‌ depth, motion, and object size perception, in dynamic‌ contexts simulating real-world situations. It provides a flexible‌ alternative to traditional experimental paradigms, maintaining high experimental‌ control while reducing the constraints associated with physical‌ setups and advanced programming.
Functional Description:
PTVR is‌ software that allows users to create and manage‌ virtual reality experiments to study visual perception. It‌ provides an easy-to-use interface to design realistic 3D‌ scenes, control objects and visual stimuli, and collect‌ data on participants’ responses. This enables researchers to‌ test how the brain and eyes perceive the‌ world under highly controlled conditions, without requiring advanced‌ programming skills or expertise in complex 3D engines.‌
Release Contributions:
PTVR is now compatible with the‌ Quest 1, 2 and 3 headsets!
News of‌ the Year:
PTVR is now compatible with the‌ Quest 1, 2 and 3 headsets!
URL:
https://ptvr.inria.fr‌
Publication:
hal-04579084
Contact:
Pierre Kornprobst
Participants:
Jeremy Termoz-Masson,‌ Eric Castet, Pierre Kornprobst, Carlos Aguilar
Partner:
Aix-Marseille‌ Université - CNRS Laboratoire de Psychologie Cognitive -‌ UMR 7290 - Team ‘Perception and attention’

7.1.5‌ Tatoovi

Name:
Tagging tool for videos
Keywords:
Annotation‌ tool, Video analysis, Multimedia player, Data visualization
Functional‌ Description:
Tatoovi is an application which provides different‌ modules for video annotation including: (1) a customizable‌ annotation dictionary with visual editor that includes fields‌ for classes, numerical values and scales, labelling, and‌ text, (2) multi-level annotation by keyframes, shots/clips, and‌ interface for film metadata and character information, (3)‌ a click-and-drag pose module to edit and visualize‌ character skeletons and bounding boxes, (4) and multi-timeline‌ visualization and intuitive video playback and navigation tools,‌ and (5) separate JSON export of timelines, annotations,‌ and dictionaries for ease of collaboration, data analysis,‌ and machine learning models
News of the Year:‌
The software was tested in a workshop with‌ 10 participants from various domains of humanities and‌ social sciences.
Contact:
Hui-Yin Wu
Participants:
Hui-Yin Wu,‌ Lucile Sassatelli, Clement Bergman, Genevieve Masioni Kibadi, Luan‌ Nguyen
Partner:
I3S

7.2 New platforms

Members of‌ Biovision are marked with a $☆$ .

7.2.1‌ CREATTIVE3D platform for navigation studies in VR

Participants:‌ $☆$ Hui-Yin Wu, $☆$ Florent Robert,‌ Lucile Sassatelli [UniCA, CNRS, I3S; Institut Universitaire de‌ France], Marco Winckler [UniCA, CNRS, I3S].‌

As part of ANR CREATTIVE3D, the Biovision team‌ has established a technological platform in the Kahn‌ immersive space including:

a 40 $m^{2}$ tracked‌ space for the Vive Pro Eye virtual reality‌ headset with 4-8 infra-red base stations,
GUsT-3D software‌ 7.1.3 under Unity for the creation, management, logging,‌ analysis, and scene graph visualization of interactive immersive‌ experiences,
Vive Pro Eye headset integrated sensors including‌ gyroscope and accelerometers, spatial tracking through base stations,‌ and 120 Hz eye tracker,
External sensors including‌ XSens Awinda Starter inertia-based motion capture costumes and‌ Shimmer GSR physiological sensors.

The platform has hosted around 60 user studies‌ lasting over 120 hours‌ this year, published in‌‌ 13. It has also provided demonstrations of‌ our virtual reality projects‌ for visitors, collaborators, and‌‌ students.

7.3 Open data

CREATTIVE3D multimodal dataset of‌ user behavior in virtual‌ reality

[doc]

Contributors:
$☆‌‌$ Hui-Yin Wu $☆$ Florent RobertLucile Sassatelli [Univ.‌ Côte d'Azur, CNRS, I3S;‌ Institut Universitaire de France]‌‌ Marco Winckler [Univ. Côte d'Azur, CNRS, I3S] Auriane‌ Gros [Univ. Côte d'Azur,‌ CoBTeK, CHU Nice] Stephen‌‌ Ramanoël [Univ. Côte d'Azur, LAMHESS]
Description:
In the‌ context of the ANR‌ CREATTIVE3D project, we join‌‌ the expertise of computer science, neuroscience, and clinical‌ practitioners, with the aim‌ to analyze the impact‌‌ that a simulated low-vision condition has on user‌ navigation behavior in complex‌ road crossing scenes: a‌‌ common daily situation where the difficulty to access‌ and process visual information‌ (e.g., traffic lights, approaching‌‌ cars) in a timely fashion can lead to‌ serious consequences on a‌ person's safety and well-being.‌‌ As a secondary objective, we also aim to‌ investigate the potential role‌ virtual reality could play‌‌ in rehabilitation and training protocols for low-vision patients.‌
Dataset PID (DOI,...):
https://zenodo.org/records/10406560‌
Project link:
https://project.inria.fr/creattive3d/
Publications:‌‌
21, 14, 6, 7
Contact:‌
Hui-Yin Wu

8 New‌ results

We present here‌‌ the new scientific results of the team over‌ the course of the‌ year. For each entry,‌‌ members of Biovision are marked with a $☆‌$ .

8.1 Modeling the‌ retina and the primary‌‌ visual system

8.1.1 Distinct inhibitory connectivity motifs trigger‌ distinct forms of anticipation‌ in the retinal network‌‌

Participants: $☆$ Simone Ebert, $☆$ Bruno Cessac‌.

Description: Motion‌ is an important feature‌‌ of visual scenes. The selection of motion features‌ starts in the retina‌ with dedicated neuronal circuits.‌‌ It has been shown that the retina can‌ extrapolate the position of‌ a moving object, thereby‌‌ compensating sensory transmission delays and enabling signal processing‌ in real-time. Amacrine cells,‌ the inhibitory interneurons of‌‌ the retina, play essential roles in such computations‌ although their precise functions‌ remain unclear. Here, we‌‌ computationally explore the effect of two different inhibitory‌ connectivity motifs on the‌ retina’s response to moving‌‌ objects: feed-forward and recurrent feed-back inhibition (see Fig.‌ 5). We show‌ that both can account‌‌ for motion anticipation with two different mechanisms. Feed-forward‌ inhibition truncates motion responses‌ and shifts peak responses‌‌ forward via subtractive inhibition, whereas recurrent feedback coupling‌ evokes, via divisive inhibition,‌ excitatory and inhibitory waves‌‌ with different phases that add up and shift‌ the response peak. A‌ key difference between the‌‌ two mechanisms is how the peak response scales‌ with the speed of‌ a moving object. Motion‌‌ prediction with feedforward circuits monotonically decreases with increasing‌ speeds, while recurrent feedback‌ coupling induces tuning curves‌‌ that exhibit a preferred speed for which motion‌ prediction is maximal.

Figure 5:
Schematic description of‌ the model and its general response properties. A.‌ The stimulus $s ( x, t)‌$ is fed into a convolution layer that simulates‌ the transformation of the visual input into a‌ neuronal voltage response, ${V}_{d r i v‌ e} (t)$ , for each Bipolar‌ Cell in the network. This convoluted signal is‌ then fed into a network of Bipolar Cells‌ and Amacrine Cells, which are reciprocally connected and‌ pass the synaptic signals $V_{B} (t‌)$ and $V_{A} (t)$ on‌ to neighboring cells of the other type. A‌ third layer of Retinal Ganglion Cells pools over‌ Bipolar Cells within their receptive field and integrate‌ their response, $R_{B} (t)$ into‌ their voltage $V_{G} (t)$ .‌ This voltage is transformed into a firing rate‌ response $R_{G} ( t)$ after rectification.‌ B. Example of step response with both connectivity‌ motifs, feed-back (purple) and feed-forward (green) inhibition. They‌ evoke a similar transient response at the onset‌ of the stimulus, which then decays to a‌ rest state, that differs between motifs. C. Rest‌ state potential for constant and spatially homogeneous inputs‌ of different amplitudes across recurrent feed-back inhibitory strength‌ $w^{-}$ . D. Rest state potential for‌ constant inputs of different amplitudes across feedforward inhibitory‌ strength $w_{A}^{G}$ . E. Example of‌ impulse response with both connectivity motifs. F. Impulse‌ responses for different recurrent inhibitory strengths $w^{-‌}$ . G. Leading Frequency of impulse response varies‌ with recurrent inhibitory strength $w^{-}$ . Same‌ color legend as in F.

This work has‌ been submitted to Scientific Reports 35.

8.1.2‌ Macular: a multi-scale simulation platform for the retina‌ and the primary visual system

Participants: $☆$ Bruno‌ Cessac, $☆$ Erwan Demairy, $☆$ Jérôme‌ Emonet, $☆$ Evgenia Kartsaki, $☆$ Thibaud‌ Kloczko, $☆$ Côme Lebreton, $☆$ Nicolas‌ Niclausse, $☆$ Selma Souihel, $☆$ Jean-Luc‌ Szpyrka, $☆$ Julien Wintz.

Description: We developed Macular, a‌ simulation platform with a‌ graphical interface, designed to‌‌ produce in silico experiment scenarios for the retina‌ and the primary visual‌ system. A scenario consists‌‌ of generating a three-dimensional structure with interconnected layers,‌ each layer corresponding to‌ a type of “cell”‌‌ in the retina or visual cortex. The cells‌ can correspond to neurons‌ or more complex structures‌‌ (such as cortical columns). The inputs are arbitrary‌ videos. The user can‌ use the cells and‌‌ synapses provided with the software, or create their‌ own using a graphical‌ interface where they enter‌‌ the constituent equations in text format (e.g., LaTeX).‌ They also create the‌ three-dimensional structure via the‌‌ graphical interface. Macular then automatically generates and compiles‌ the C++ code and‌ generates the simulation interface.‌‌ This allows the user to view the input‌ video and the three-dimensional‌ structure in layers. It‌‌ also allows the user to select cells and‌ synapses in each layer‌ and view the activity‌‌ of their state variables. Finally, the user can‌ adjust the phenomenological parameters‌ of the cells or‌‌ synapses via the interface. We provide several example‌ scenarios, corresponding to published‌ articles, including an example‌‌ of a retino-cortical model. Macular was designed for‌ neurobiologists and modelers, specialists‌ in the primary visual‌‌ system, who want to test hypotheses in silico‌ without the need for‌ programming. By design, this‌‌ tool allows natural or altered conditions (pharmacology, pathology,‌ development) to be simulated.‌

Figure 6 illustrates an‌‌ example of simulation with our platform. This paper‌ has been accepted for‌ publication in the journal‌‌ Frontiers in Neuroinformatics19.

Figure 6: The‌‌ retino-cortical scenario. The upper left panel shows the‌ heatmap of the 5‌ cell types. Bipolar cells‌‌ with gain control appear in blue, amacrine in‌ magenta, ganglion cells with‌ gain control in yellow,‌‌ excitatory population of cortical columns in green and‌ inhibitory population of cortical‌ columns in red. On‌‌ the panel below, left, one sees the video‌ of the stimulus, a‌ white bar moving. Right‌‌ panels are plots of‌ Cells activity. The upper one displays retinal outputs‌ : bipolar voltage (red), amacrine voltage (yellow) and‌ ganglion cells firing rate (pink). The bottom panel‌ displays cortical output : excitatory (blue) and inhibitory‌ (orange) mean voltage.

8.1.3 A chimera model for‌ motion anticipation in the retina and the primary‌ visual cortex

Participants: $☆$ Jérôme Emonet, Selma‌ Souihel [Inria, P16 - Programme IA], $☆‌$ Bruno Cessac, Alain Destexhe [NeuroPSI - Institut‌ des Neurosciences Paris-Saclay], Frédéric Chavane [INT -‌ Institut de Neurosciences de la Timone], Matteo‌ Di Volo [UCBL - Université Claude Bernard Lyon‌ 1].

Description: Motion anticipation refers to‌ the capacity of the visual system to compensate‌ for inherent delays in visual processing. This ability‌ results from distinct mechanisms taking place in the‌ retina 43 and in the visual cortex 52‌. To study their respective role, we propose‌ a mean field model of the primary visual‌ cortex (V1) connected to a realistic retina model.‌ Our first goal is to reproduce experimental results‌ on motion anticipation, made in monkeys by using‌ voltage dye optical imaging (VSDI) 42, and‌ to assess the impact of the retina in‌ this process. For this, we first study the‌ model in the case where the retina does‌ not itself provide anticipation. Then, anticipation is only‌ triggered by a cortical mechanism, called "anticipation by‌ latency". As we show, this mechanism strongly depends‌ on the intensity of the retinal input supplied‌ to the cortex, even if the retina does‌ not itself provide anticipation. We also unravel the‌ effect of the stimulus features, such as speed‌ and contrast, and report the impact of physiological‌ parameters not accessible experimentally, such as the size‌ of cortical extensions or fibre conduction. Then, we‌ explore the changes in the cortical wave of‌ anticipation when V1 is triggered by a retina‌ output implementing different potential retina-driven anticipatory mechanisms, including‌ gain control and lateral inhibition by amacrine cells.‌ In this setting, we show how retinal and‌ cortical anticipation combine, to provide an efficient processing‌ where the VSDI signal response is in advance‌ over the moving object that triggers this response,‌ compensating the delays in visual processing, in full‌ agreement with the experimental results of 42.‌ This work has been accepted in the journal‌ Neural Computation20. An example of results‌ is shown in Fig. 7

Figure 7:
The effect‌ of bipolar gain control‌ strength, $h_{B}$ ,‌‌ on the cortical response. (see the paper 20‌ for detail.) VSDI signal‌ response to bipolar gain‌‌ control at A) 0 mV/s (control) and B)‌ $9.2$ mV/s.‌C) Temporal VSDI signal‌‌ in response to increasing bipolar gain control for‌ the cortical column located‌ at the center of‌‌ the layer ( $x = 9, y‌ = 1 . 35‌$ ). D) Spatial VSDI‌‌ signal in response to increasing bipolar gain control,‌ for the time where‌ the central cortical column‌‌ reaches its maximum. The $x$ coordinates has been‌ shifted so that the‌ central cortical column is‌‌ actually located at $x = 0$ . E)‌ VSDI signal amplitude of‌ the central cortical column‌‌ versus $h_{B}$ . F) Temporal and spatial‌ observables: anticipation range (green)‌ and maximal latency (red)‌‌ versus $h_{B}$ . G) Speed observable :‌ short-range activation speed (red)‌ in function of bipolar‌‌ gain control weight. In A, B, C, we‌ also drawn the Equivalent‌ Retinal Output Amplitude (EROA)‌‌ curve. This is the dotted black curve with‌ the same colored symbols.‌ H) Stationary peak delay‌‌ (SPD) for RGCs (Retinal Ganglion Cells, orange), VSDI‌ signal (blue) for the‌ central cell (scales on‌‌ the left) and difference between RGC SPD and‌ VSDI signal SPD (black,‌ scales on the right).‌‌ I) Shape of the central RGC response profile‌ to the moving bar‌, without gain control‌‌ (red) and with BC gain control $9 .‌ 165$ mV/s (dashed green).‌ Note that the two‌‌ traces have been rescaled to have the same‌ maximum. This is to‌ emphasize the change in‌‌ the shape of the response induced by BCs‌ gain control. J) VSDI‌ signal, same conditions.‌‌ In I,J, the dotted lines correspond to the‌ peaks in the RGC‌ firing rate or VSDI‌‌ signal without BCs gain control (red) and with‌ it (green).

8.1.4 The‌ refresh rate of overhead‌‌ projectors may affect the perception of fast moving‌ objects: a modeling study‌

Participants: $☆$ Bruno Cessac‌‌, $☆$ Jérôme Emonet.

Description:

Using‌ simulation and a simple‌ mathematical argument we argue‌‌ that the refresh rate of overhead projectors, used‌ in experiments on the‌ visual system, may impact‌‌ the perception of fast moving objects already at‌ the retinal and cortical‌ level (V1). We use‌‌ the retino-cortical (V1) model 20 and carry out‌ simulations featuring the retina‌ and V1 response to‌‌ a fast bar moving at $200 / s‌$ projected to the retina‌ with a high (1440‌‌ Hz) or low (60 Hz) frame rate. In‌ this context, we observe‌ a difference in response‌‌ to a fast moving object baring analogies with‌ what was observed in‌ psychophysics. In addition, we‌‌ show how a simple mechanism of linear integration‌ can explain what we‌ observe. Figure 8 illustrates‌‌ our results.

Figure 8.a — Figure 8:
Top. The simulated‌ retinal activity generated by a fast movement is‌ strongly influenced by the frame rate. For each‌ figure, the time axis is represented by the‌ top purple arrow. We display time snapshots indicated‌ below the time axis. Black rectangles show the‌ successive positions of the bar. Color images show‌ the RGCs activity (color map) at times indicated‌ on the top. The first five images are‌ the first five frames of our video, in‌ order to show the bar positions (in the‌ fifth frame it has already left the visual‌ field). The other ten images are frames separated‌ by a longer time interval to illustrate the‌ integration by RGCs. A) Frame rate of 60‌ Hz.B) Frame rate of 1440 Hz.Bottom.‌ The simulated cortical activity generated by a fast‌ movement is strongly influenced by the frame rate.‌ Same representation as above but here the color‌ maps correspond to cortical activity (VSDI).

Figure 8.b — Figure 8:
Top. The simulated‌ retinal activity generated by a fast movement is‌ strongly influenced by the frame rate. For each‌ figure, the time axis is represented by the‌ top purple arrow. We display time snapshots indicated‌ below the time axis. Black rectangles show the‌ successive positions of the bar. Color images show‌ the RGCs activity (color map) at times indicated‌ on the top. The first five images are‌ the first five frames of our video, in‌ order to show the bar positions (in the‌ fifth frame it has already left the visual‌ field). The other ten images are frames separated‌ by a longer time interval to illustrate the‌ integration by RGCs. A) Frame rate of 60‌ Hz.B) Frame rate of 1440 Hz.Bottom.‌ The simulated cortical activity generated by a fast‌ movement is strongly influenced by the frame rate.‌ Same representation as above but here the color‌ maps correspond to cortical activity (VSDI).

8.2 Diagnosis,‌ rehabilitation, and low-vision aids

8.2.1 Central field loss‌ patients' ability to select targets with head-pointing using‌ virtual reality: An exploratory psychophysical study

Participants: Camille‌ Bordeau [Aix Marseille Univ, CNRS, CRPN, Marseille, France]‌, Célia Passerel [Aix Marseille Univ, CNRS, CRPN,‌ Marseille, France], Carlos Aguilar [Clubdes3], Iliana‌ Huyet [Centre Paradis Monticelli, Marseille], Caroline Topart‌ [Centre Paradis Monticelli, Marseille], François Devin [Centre‌ Paradis Monticelli, Marseille], Frédéric Matonti [Centre Paradis‌ Monticelli, Marseille], $☆$ Pierre Kornprobst [Univ. Côte‌ d'Azur / BIOVISION], Eric Castet [Aix Marseille‌ Univ, CNRS, CRPN, Marseille, France].

Context: People‌ with central field loss (CFL) experience increased spatial‌ uncertainty in peripheral vision, making accurate selection of‌ visual targets particularly challenging. This limitation is critical‌ in virtual reality (VR) environments, where selection mechanisms‌ are essential for interacting with objects, menus, and‌ visual aids. Designing efficient and accessible selection techniques‌ adapted to CFL users is therefore a key‌ requirement for autonomy in immersive systems.

Description: In‌ this exploratory psychophysical study 30, we investigated‌ the ability of CFL patients to select visual‌ targets using head-pointing in a VR environment. A‌ two-step selection technique specifically designed for CFL users‌ was implemented using the open-source PTVR toolbox and‌ evaluated in an HTC Vive Pro headset. The‌ selection process consisted of a pre-selection step, in‌ which participants moved their head until a head-contingent‌ reticle surrounded the target and induced a visual‌ flicker, followed by a validation step requiring stable head positioning for 1500‌ ms. Task difficulty was‌ manipulated by varying the‌‌ diameter of an invisible Pointer Activation Zone (PAZ),‌ defining the tolerance area‌ for pre-selection. Twenty-four CFL‌‌ patients and nineteen age-matched control participants were tested‌ under monocular viewing conditions.‌ Results showed that selection‌‌ time was significantly longer for CFL patients than‌ for controls and decreased‌ asymptotically with increasing PAZ‌‌ diameter. The estimated critical PAZ diameter was substantially‌ larger for patients, reflecting‌ their higher spatial uncertainty.‌‌ These findings provide quantitative insights into head-pointing performance‌ in CFL and offer‌ methodological guidelines for the‌‌ design of robust selection tools in VR visual‌ aids for low-vision users.‌

This work was presented‌‌ at the 15th International Conference on Low Vision‌ Research and Rehabilitation, held‌ in September 2025 in‌‌ Florence, Italy 30.

8.2.2 An asymmetric VR‌ system to configure and‌ practice low-vision aids for‌‌ social interactions in clinical settings

Participants: $☆$ Johanna‌ Delachambre, $☆$ Hui-Yin‌ Wu, $☆$ Pierre‌‌ Kornprobst, Monica Di Meo [CHU - Hôpital‌ Pasteur, Nice], Frédérique‌ Lagniez [CHU - Hôpital‌‌ Pasteur, Nice], Christine Morfin-Bourlat [CHU - Hôpital‌ Pasteur, Nice], Stéphanie‌ Baillif [CHU - Hôpital‌‌ Pasteur, Nice], Eric Castet [Aix Marseille Univ,‌ CNRS, CRPN, Marseille, France]‌.

Context: Patients with‌‌ visual impairment commonly rely on low-vision aids (LVAs),‌ such as magnifiers, to‌ perform near-vision tasks. Rehabilitation‌‌ programs traditionally focus on these activities, while social‌ interactions remain largely underexplored,‌ despite their major impact‌‌ on patients’ autonomy and quality of life. Extended‌ reality (XR) technologies, and‌ virtual reality (VR) in‌‌ particular, offer promising opportunities to support training and‌ guidance in more complex,‌ ecologically valid situations.

Description:‌‌ In 24, we present an asymmetric VR‌ system designed to configure‌ and practice the use‌‌ of low-vision aids within immersive social interaction scenarios‌ (see Fig. 9).‌ The system enables clinicians‌‌ and orthoptists to supervise and adapt the experience‌ while patients are immersed‌ in VR, allowing realistic‌‌ testing of LVAs under controlled yet flexible conditions.‌ Through an observational study‌ involving visually impaired patients‌‌ and orthoptists, we show how this approach complements‌ and extends current clinical‌ practices. The proposed system‌‌ facilitates personalization of rehabilitation strategies, supports efficient training‌ of LVAs for social‌ contexts, and enables the‌‌ exploration of situations that are difficult to reproduce‌ in standard clinical settings.‌ Overall, this work illustrates‌‌ how immersive technologies can contribute to a more‌ holistic approach to low-vision‌ rehabilitation, integrating both functional‌‌ and social dimensions.

This work was presented as‌ a poster at the‌ IEEE VR 2025 conference‌‌ (32nd IEEE Conference on Virtual Reality and 3D‌ User Interfaces), held in‌ March 2025 in Saint-Malo,‌‌ France 24.

Figure 9:
Overview of the asymmetric‌ system involving (1) a patient in virtual reality‌ (VR), equipped with low-vision aids (LVAs), immersed in‌ scenes with various characters; and (2) an orthoptist‌ using a tablet to select and configure different‌ LVAs. This system was tested in a social‌ interaction context in VR.

8.2.3 Select and Augment‌ Segmented Items (SASI): An item-based magnification approach for‌ dynamic face recognition for people with central vision‌ loss

Participants: $☆$ Johanna Delachambre, $☆$ Hui-Yin‌ Wu, Monica Di Meo [CHU - Hôpital‌ Pasteur, Nice], Frédérique Lagniez [CHU - Hôpital‌ Pasteur, Nice], Christine Morfin-Bourlat [CHU - Hôpital‌ Pasteur, Nice], Stéphanie Baillif [CHU - Hôpital‌ Pasteur, Nice], Aurélie Calabrèse [Aix Marseille Univ,‌ CNRS, CRPN, Marseille, France], Eric Castet [Aix‌ Marseille Univ, CNRS, CRPN, Marseille, France], $☆‌$ Pierre Kornprobst.

Context: Supporting social interactions remains‌ a major challenge for people with central vision‌ loss, particularly when it involves recognizing moving faces‌ in dynamic environments. Traditional magnification approaches are often‌ head-centered and can be inefficient or cognitively demanding‌ in such situations. Recent interaction principles, such as‌ item-based magnification, have shown promise in static 2D‌ contexts, but their applicability to dynamic, three-dimensional scenarios‌ has not yet been demonstrated.

Description: In 34‌, we investigate magnification strategies for dynamic face‌ recognition in the context of social interaction for‌ people with central vision loss. We introduce Select‌ and Augment Segmented Items (SASI), an item-based magnification‌ approach that allows users to select specific segmented‌ items—in this study, faces—and stabilize their magnified representation‌ in space, facilitating detailed visual analysis. Building on‌ these principles, we propose two SASI-based variants specifically‌ adapted to dynamic 3D environments. Using virtual reality‌ (VR) as a controlled yet ecologically valid experimental‌ platform, we compare SASI magnifiers with traditional head-centered‌ magnification in a face recognition task involving moving‌ avatars. Our results show that SASI-based approaches lead‌ to significantly more accurate face recognition, demonstrating clear‌ advantages in dynamic scenarios. This work is the‌ first to validate the effectiveness of SASI principles‌ in complex, dynamic social contexts, highlighting their strong‌ potential to enhance visual support for social interaction‌ in low-vision populations.

This work has been submitted‌ to an international peer-reviewed journal.

Figure 10:‌
SASI magnifier in action.‌ (a) The user points‌‌ at a desired face using a head-contingent white‌ pointer. When the pointer‌ intersects with a Region‌‌ of Interest (ROI)—here, a face—the ROI becomes pre-selected‌ (yellow circle), indicating that‌ it can be augmented.‌‌ (b) Once augmented, the ROI appears as the‌ Region of Augmented Vision‌ (ROAV). (c) In the‌‌ SASI-Stat variant, the ROAV remains anchored at the‌ face’s position at the‌ moment of its activation,‌‌ staying fixed in the environment as the person‌ moves. (d) In the‌ SASI-Dyn variant, the ROAV‌‌ remains anchored to the face, dynamically following its‌ motion. In both cases,‌ by anchoring the magnified‌‌ view to the world rather than to the‌ user’s head, SASI allows‌ users with central vision‌‌ loss (CVL) to explore the ROAV freely with‌ their gaze without it‌ being affected by head‌‌ movements, improving stability and comfort.

8.2.4 Reading magnified‌ newspapers versus newspapers magnification:‌ A new method to‌‌ address the local/global navigation problem on digital devices‌

Participants: Sebastian Gallardo [BIOVISION]‌, Aurélie Calabrèse [CRPN]‌‌, Monica Di Meo [CHU - Hôpital Pasteur,‌ Nice], Frédérique Lagniez‌ [CHU - Hôpital Pasteur,‌‌ Nice], Christine Morfin-Bourlat [CHU - Hôpital Pasteur,‌ Nice], Stéphanie Baillif‌ [CHU Nice], $☆‌‌$ Hui-Yin Wu [BIOVISION], Dorian Mazauric, $☆‌$ Pierre Kornprobst [Univ. Côte‌ d'Azur / BIOVISION].‌‌

Context: Numerous accessibility features have been proposed to‌ support digital reading for‌ low-vision individuals, including text‌‌ magnification, spacing adjustments, font changes, and contrast enhancements.‌ While these solutions are‌ effective for linear documents,‌‌ navigating complex layout-based documents such as newspapers remains‌ challenging. Readers must not‌ only access textual content‌‌ but also preserve a global understanding of the‌ page structure in order‌ to skim, orient themselves,‌‌ and efficiently locate relevant information.

Description: This work‌ builds upon an algorithmic‌ approach for newspaper magnification‌‌ with preserved entry points, in which layout-based‌ documents are magnified while‌ maintaining the readability and‌‌ visual salience of key entry points such as‌ headlines. This approach was‌ introduced in 51,‌‌ where the problem is formulated as a novel‌ two-dimensional packing problem integrating‌ computational aesthetics criteria and‌‌ solved using a genetic algorithm. The method generates‌ alternative large-print layouts that‌ keep all articles visually‌‌ present on the page, addressing the loss of‌ global awareness induced by‌ standard pinch-to-zoom interactions.

In‌‌ 22, we focused on the experimental evaluation‌ of this approach through‌ a user study comparing‌‌ it with conventional gesture-based magnification. Using a personalized‌ magnification factor derived from‌ each participant’s Critical Print‌‌ Size (CPS), measured with the MNREAD test, we‌ evaluated reading behavior under‌ two conditions: a standard‌‌ pan-and-zoom interaction on the original layout, and a‌ large-print, layout-aware version providing‌ direct access to magnified‌‌ headlines. Normally sighted and‌ low-vision participants performed reading and visual search tasks‌ on multiple newspaper pages. Objective performance metrics (reading‌ time, navigation behavior) and subjective feedback (comfort, perceived‌ workload) were collected to assess the trade-off between‌ manual interaction and global awareness. The results highlight‌ the benefits and limitations of both approaches and‌ provide insights into how layout-aware magnification strategies can‌ improve accessibility for digital newspapers and other structured‌ documents.

The algorithmic foundations of this work are‌ described in a research report corresponding to a‌ manuscript currently under submission 51. This work‌ was also presented at the SophI.A Summit in‌ November 2025 in Sophia Antipolis, France 31.‌ The experimental study was presented at the 15th‌ International Conference on Low Vision Research and Rehabilitation,‌ held in September 2025 in Florence, Italy 22‌.

Figure 11:‌ Experimental protocol for comparing reading behavior under visual‌ constraints in layout-based documents. The figure illustrates the‌ two conditions used in the study to evaluate‌ the trade-off between manipulable interaction and global awareness‌ when reading digital newspapers. In the gesture-based magnification‌ condition (GB), participants read the original layout, where‌ headlines are rendered below the participant’s Critical Print‌ Size (CPS) and require pan-and-zoom interaction. In the‌ large-print edition with direct access condition (LP), headlines‌ are enlarged above CPS and directly legible without‌ magnification. Across multiple trials, participants performed headline reading‌ and article search tasks, while objective and subjective‌ measures were collected.

8.3 Visual media analysis and‌ creation

8.3.1 Exploring, walking, and interacting in virtual‌ reality with simulated low vision: a living contextual‌ dataset

Participants: $☆$ Hui-Yin Wu, $☆$ Florent‌ Robert, $☆$ Franz Franco Gallo, $☆‌$ Kateryna Pirkovets, Clément Quéré [Univ. Côte d'Azur,‌ CNRS, I3S], $☆$ Johanna Delachambre, Stephen‌ Ramanoël [Univ. Côte d'Azur, LAMHESS], Auriane Gros‌ [Univ. Côte d'Azur, CoBTeK], Marco Winckler [Univ.‌ Côte d'Azur, Polytech, CNRS, I3S], Lucile Sassatelli‌ [Univ. Côte d'Azur, CNRS, I3S], Meggy Hayotte‌ [Univ. Côte d'Azur, LAMHESS], Aline Menin [Univ.‌ Côte d'Azur, CNRS, I3S], $☆$ Pierre Kornprobst‌.

Description: We have published the CREATTIVE3D open‌ dataset of human interaction and navigation at road‌ crossings in virtual reality. The dataset has three‌ main breakthroughs: (1) it is the largest dataset‌ of human motion in fully-annotated scenarios (40 hours,‌ 2.6 million poses), (2) it is captured in dynamic 3D scenes with‌ multivariate-gaze, physiology, and motion-data,‌ and (3) it investigates‌‌ the impact of simulated low-vision conditions using dynamic‌ eye tracking under real‌ walking and simulated walking‌‌ conditions. Extensive effort has been made to ensure‌ the transparency, usability, and‌ reproducibility of the study‌‌ and collected data, even under extremely complex study‌ conditions involving 6 degrees‌ of freedom interactions, and‌‌ multiple sensors. We believe this will allow studies‌ using the same or‌ similar protocols to be‌‌ comparable to existing study results, and allow a‌ much more fine-grained analysis‌ of individual nuances of‌‌ user behavior across datasets or study designs. This‌ is what we call‌ a living contextual dataset.‌‌

This work was published in Nature Scientific Data‌ 21 with the dataset‌ available on Zenodo: CREATTIVE3D‌‌ multimodal dataset of user behavior in virtual reality‌.

8.3.2 The triangle‌ of misunderstanding in interactive‌‌ virtual narratives: gulfs between system, designers and players‌

Participants: $☆$ Florent Robert‌, $☆$ Hui-Yin Wu‌‌, Marco Winckler [Univ. Côte d'Azur, Polytech, CNRS,‌ I3S], Lucile Sassatelli‌ [Univ. Côte d'Azur, CNRS,‌‌ I3S].

Context: Virtual Reality (VR) technologies enable‌ strong emotions compared to‌ traditional media, stimulating the‌‌ brain in ways comparable to real-life interactions. This‌ makes VR systems promising‌ for research and applications‌‌ in training or rehabilitation, to imitate realistic situations.‌ Nonetheless, the evaluation of‌ the user experience in‌‌ immersive environments is daunting, the richness of the‌ media presents challenges to‌ synchronize context with behavioral‌‌ metrics in order to provide fine-grained personalized feedback‌ or performance evaluation. The‌ variety of scenarios and‌‌ interaction modalities multiply this difficulty of user understanding‌ in the face of‌ lifelike training scenarios, complex‌‌ interactions, and rich context.

Figure 12: Our model‌ was inspired by Norman‌ 55 to characterize the‌‌ designer-system-player communication gulfs, allowing the definition of a‌ taxonomy of eight issues‌ sources of gulfs happening‌‌ during the design, usage, and interpretation phases.

Description:‌ Designers of storytelling experiences‌ in virtual reality (VR)‌‌ can take advantage of the medium’s realism and‌ immersion to communicate their‌ intentions. However, interaction freedom‌‌ comes with unpredictability, raising the risk of miscommunication‌ between the experience sought‌ by the designer and‌‌ the player’s interpretation. To better understand such miscommunications,‌ we revisit Don Norman’s‌ work on stages of‌‌ action 55 to propose a model of designer-player‌ gulfs in VR that‌ incorporates eight classes of‌‌ communication gulfs. We designed a two-phase study where‌ 10 participants designed VR‌ scenarios and then played‌‌ scenarios created by previous participants. Through coupled structured‌ interviews, we identified 127‌ issues in VR-mediated communication‌‌ that were mapped to our model to understand‌ their impact on the‌ player’s interpretation of the‌‌ narrative experience. Our work provides a roadmap to‌ identifying sources of miscommunication‌ in VR, a first‌‌ step to conceiving principles and guidelines for achieving‌ effective communication in storytelling‌ experiences.

This work was‌‌ published at the 2025‌ ACM Conference on Human Factors (SIGCHI) 27.‌

8.3.3 SILOVIS: SImulating LOw Vision through Immersive Storytelling‌

Participants: Damien Dechambre [Inria], $☆$ Hui-Yin Wu‌, $☆$ Pierre Kornprobst, $☆$ Grégoire Arrabie-Aubies‌.

Description: This project is situated in the‌ context of a joint project between ANR CREATTIVE3D‌ (Inria Biovision team) and Handitechlab Inria, with the‌ objective to develop an application in virtual reality‌ (VR) to simulate visual impairments and their impact‌ on various everyday activities. Specifically, low-vision conditions refer‌ to visual impairments that cannot be full relieved‌ through corrective lenses nor surgical procedures, strongly impacting‌ daily interactions and activities of people suffering from‌ these conditions. Currently, due to their invisibility, these‌ impacts are often not well-understood by the general‌ public nor by services with whom patients must‌ interact. With the high level of immersion and‌ interactivity afforded by VR, there is a strong‌ interest to harness VR technologies to create realistic‌ simulations that are representative of the difficulties and‌ challenges faced by people living with visual handicaps.‌

In continuation of projects such as AMDJournee, we‌ collaborate with Damien Dechambre and members of the‌ Inria HandiTechLab team to work on the continued‌ development of a visual impairement simulator coupled with‌ interactive 3D scenes and scenarios in VR. The‌ expected outcome of this project involves a VR‌ application that simulates low-vision conditions in realistic, interactive‌ scenarios, targeted towards raising public awareness. The simulation‌ will have strong applications towards professional training, accessible‌ content, product, and spatial design. In addition, this‌ simulation allows researchers to study how vision impairments‌ affect spatial awareness, navigation, and task performance in‌ virtual environments, with the broader goal to ultimately‌ inform the design of assistive technologies and rehabilitation‌ strategies. A first prototype of this application has‌ been developed (Figure 13) and presented at‌ various venues for digital accessibility and low-vision awareness.‌

Figure 13: A first version of‌ our application titled "Dans les yeux d'Alice" (Through‌ the Eyes of Alice) was developed featuring numerous‌ scenarios showing the experience of living with low-vision‌ conditions such as tunnel vision such as (A)‌ relying on metal maps to keep one's bearing‌ in the environment, (B) animation depicting the worsening‌ of the condition over time, and (C) the‌ effect of environmental lighting that can cause over exposure or lack of‌ contrast, both of which‌ can augment eye fatigue.‌‌

9 Bilateral contracts and grants with industry

9.1‌ Bilateral contracts with industry‌

Participants: $☆$ Pierre Kornprobst‌‌, $☆$ Sebastián Gallardo [Demain un Autre Jour]‌, Dorian Mazauric [Univ.‌ Côte d'Azur, Inria, ABS‌‌ Team].

CIFRE contract with the company Demain‌ Un Autre Jour (directed‌ by Bruno Génuit), in‌‌ the framework of the PhD project of Sebastián‌ Gallardo, co-supervised by Pierre‌ Kornprobst and Dorian Mazauric‌‌ (June 2023 – May 2026). The PhD topic‌ is “Rethinking Newspaper Production:‌ Optimizing Workflows with AI".‌‌

10 Partnerships and cooperations

10.1 International initiatives

10.1.1‌ Associate Teams in the‌ framework of an Inria‌‌ International Lab or in the framework of an‌ Inria International Program

Title:‌
Functional structure of the‌‌ retina: A physiological and computational approach
Duration:
January‌ 1, 2024 –
Local‌ supervisor:
Bruno Cessac
Partners:‌‌
- Adrian Palacios, Universidad de Valparaiso (Chile)
Inria contact:‌
Bruno Cessac
Summary:
This‌ project aims to gain‌‌ a better understanding of the retinal response to‌ complex visual stimuli, and‌ to discover the role‌‌ played by the network of lateral interneurons (amacrine‌ cells) in this response.‌ To achieve this, we‌‌ will adopt a dual methodology: experimental and computational.‌ Using real-time control and‌ a feedback loop, we‌‌ will exploit a computer model of the retina‌ to adapt, in real‌ time, visual stimuli to‌‌ the recorded responses of retinal cells. The experiments‌ will be carried out‌ in Valparaiso (Chile). The‌‌ Biovision team will develop the control software and‌ extrapolate the results, drawing‌ on its recent theoretical‌‌ advances in retinal modeling. This is a transdisciplinary,‌ international project at the‌ interface between biology, computer‌‌ science and mathematical neuroscience. Beyond a better understanding‌ of the role of‌ its network structure in‌‌ the retina's response to complex spatio-temporal stimuli, this‌ project could potentially have‌ an impact on diagnostic‌‌ methods for neurodegenerative diseases such as Alzheimer's.

10.1.2‌ STIC/MATH/CLIMAT AmSud projects

CANARD‌

Title:
Computational Approaches in‌‌ Neuroscience for Aging and Retinal NeuroDegeneration
Program:
STIC-AmSud‌
Duration:
January 1, 2025‌ – December 31, 2026‌‌
Local supervisor:
Bruno Cessac
Partners:
- Adrian Palacios (Chili)‌
- Sergio Neuenschwander (Brésil)
- Evelyn‌ Aviles (Chili)
- Jérôme Baron‌‌ (Brésil)
Inria contact:
Bruno Cessac
Summary:
This project‌ is designed to uncover‌ the fundamental mechanisms behind‌‌ retinal aging and neurodegenerative processes by employing a‌ collaborative method that combines‌ sophisticated computational modeling with‌‌ state-of-the-art experimental methods. Disorders related to aging in‌ the retina and neurodegenerative‌ conditions present substantial challenges‌‌ to worldwide health systems, further intensified by a‌ growing aging population. Present‌ models of these diseases‌‌ are not comprehensive, which hampers progress in treatments.‌ The projet will leverage‌ computational models of the‌‌ retina and multielectrode arrays (MEA) experiments to both‌ simulate and monitor the‌ aging process and neurodegenerative‌‌ alterations in retinal cells. The computational efforts, headed‌ by Dr. Bruno Cessac,‌ will develop dynamic models‌‌ of the retinal neural network, improving our comprehension‌ of how cells interact‌ over time. MEA experiments‌‌ will be carried out‌ in the laboratories of Dr. Sergio Neuenschwander and‌ Dr. Adrian Palacios to gather live data from‌ aging retinal cells, aiding in the validation and‌ enhancement of computational models. Dr. Evelyn Aviles will‌ provide genetically engineered mouse models that display the‌ pathological signs of retinal neurodegeneration, effectively linking theoretical‌ forecasts with actual findings.

10.2 International research visitors‌

10.2.1 Visits of international scientists

Other international visits‌ to the team

Simone Ebert

Status:
Postdoc
Institution‌ of origin:
University of Tübingen
Country:
Germany
Dates:‌
07/07/25-11/07/25
Context of the visit:
Collaboration
Mobility program/type‌ of mobility:

Andrès Navaro

Status
intern (master/eng)
Institution‌ of origin:
University of Valparaiso
Country:
Chile
Dates:‌
01/02/25-30/05/25
Context of the visit:
Fusion Associated Team,‌ internship DRISI
Mobility program/type of mobility:
internship DRISI‌

10.2.2 Visits to international teams

Research stays abroad‌

B. Cessac stayed in Valparaiso, Chile, from 11/01/2025‌ to 24/01/2025 in the context of the Inria‌ Fusion associated team and ESTHETICS Univ. Côte d'Azur‌ project.
E. Petit stayed in Valparaiso, Chile, from‌ 04/01/2025 to 31/01/2025 in the context of the‌ Inria Fusion associated team and ESTHETICS Univ. Côte‌ d'Azur project.
L. Piovano stayed in Valparaiso, Chile,‌ from 10/11/2025 to 22/11/2025 in the context of‌ the CANARD project.

10.3 National initiatives

Participants: $☆‌$ Bruno Cessac, $☆$ Pierre Kornprobst, $☆‌$ Hui-Yin Wu.

10.3.1 ANR

ShootingStar

Title:
Processing‌ of naturalistic motion in early vision
Programme:
ANR‌
Duration:
April 2021 - March 2025
Coordinator:
Mark‌ WEXLER (CNRS‐INCC),
Partners:
- Institut de Neurosciences de la‌ Timone (CNRS and Aix-Marseille Université, France)
- Institut de‌ la Vision (IdV), Paris, France
- Unité de Neurosciences‌ Information et Complexité, Gif sur Yvette, France
- Laboratoire‌ Psychologie de la Perception - UMR 8242, Paris‌
Inria contact:
Bruno Cessac
Summary:
The natural visual‌ environments in which we have evolved have shaped‌ and constrained the neural mechanisms of vision. Rapid‌ progress has been made in recent years in‌ understanding how the retina, thalamus, and visual cortex‌ are specifically adapted to processing natural scenes. Over‌ the past several years it has, in particular,‌ become clear that cortical and retinal responses to‌ dynamic visual stimuli are themselves dynamic. For example,‌ the response in the primary visual cortex to‌ a sudden onset is not a static activation,‌ but rather a propagating wave. Probably the most‌ common motions in the retina are image shifts‌ due to our own eye movements: in free‌ viewing in humans, ocular saccades occur about three‌ times every second, shifting the retinal image at‌ speeds of 100-500 degrees of visual angle per‌ second. How these very fast shifts are suppressed,‌ leading to clear, accurate, and stable representations of‌ the visual scene, is a fundamental unsolved problem‌ in visual neuroscience known as saccadic suppression. The‌ new Agence Nationale de la Recherche (ANR) project‌ “ShootingStar” aims at studying the unexplored neuroscience and‌ psychophysics of the visual perception of fast (over‌ 100 deg/s) motion, and incorporating these results into‌ models of the early visual system.

DEVISE

Title:
From novel rehabilitation protocols‌ to visual aid systems‌ for low vision people‌‌ through Virtual Reality
Programme:
ANR
Duration:
2021–2025
Coordinator:‌
Eric Castet (Laboratoire de‌ Psychologie Cognitive, Marseille)
Partners:‌‌
- CNRS/Aix Marseille University – AMU, Cognitive Psychology Laboratory‌
- AMU, Mediterranean Virtual Reality‌ Center
Inria contact:
Pierre‌‌ Kornprobst
Summary:
The ANR DEVISE (Developing Eccentric Viewing‌ in Immersive Simulated Environments)‌ aims to develop in‌‌ a Virtual Reality headset new functional rehabilitation techniques‌ for visually impaired people.‌ A strong point of‌‌ these techniques will be the personalization of their‌ parameters according to each‌ patient’s pathology, and they‌‌ will eventually be based on serious games whose‌ practice will increase the‌ sensory-motor capacities that are‌‌ deficient in these patients.

CREATTIVE3D

Title:
Creating attention‌ driven 3D contexts for‌ low vision
Programme:
ANR‌‌
Duration:
2022–2026
Coordinator:
Hui-Yin Wu
Partners:
- Université Côte‌ d'Azur I3S, LAMHESS, CoBTEK‌ laboratories
- CNRS/Aix Marseille University‌‌ – AMU, Cognitive Psychology Laboratory
Summary:
CREATTIVE3D deploys‌ virtual reality (VR) headsets‌ to study navigation behaviors‌‌ in complex environments under both normal and simulated‌ low-vision conditions. We aim‌ to model multi-modal user‌‌ attention and behavior, and use this understanding for‌ the design of assisted‌ creativity tools and protocols‌‌ for the creation of personalized 3D-VR content for‌ low vision training and‌ rehabilitation.

TRACTIVE

Title:
Towards‌‌ a computational multimodal analysis of film discursive aesthetics‌
Programme:
ANR
Duration:
2022–2026‌
Coordinator:
Lucile Sassatelli
Partners:‌‌
- Université Côte d'Azur CNRS I3S
- Université Côte d'Azur,‌ CNRS BCL
- Sorbonne Université,‌ GRIPIC
- Université Toulouse 3,‌‌ CNRS IRIT
- Université Sorbonne Paris Nord, LabSIC
Inria‌ contact:
Hui-Yin Wu
Summary:‌
TRACTIVE's objective is to‌‌ characterize and quantify gender representation and women objectification‌ in films and visual‌ media, by designing an‌‌ AI-driven multimodal (visual and textual) discourse analysis. The‌ project aims to establish‌ a novel framework for‌‌ the analysis of gender representation in visual media.‌ We integrate AI, linguistics,‌ and media studies in‌‌ an iterative approach that both pinpoints the multimodal‌ discourse patterns of gender‌ in film, and quantitatively‌‌ reveals their prevalence. We devise a new interpretative‌ framework for media and‌ gender studies incorporating modern‌‌ AI capabilities. Our models, published through an online‌ tool, will engage the‌ general public through participative‌‌ science to raise awareness towards gender-in-media issues from‌ a multi-disciplinary perspective.

Participants:‌ Bruno Cessac, Pierre‌‌ Kornprobst, Hui-Yin Wu, Jérôme Emonet,‌ Franz Franco Gallo,‌ Erwan Petit, Laura‌‌ Piovano, Paritosh Sharma.

11 Dissemination

11.1‌ Promoting scientific activities

11.1.1‌ Scientific events: organization

Reviewer‌‌

H.-Y. Wu was a reviewer for the IEEE‌ Conference on Acoustics, Speech,‌ and Signal Processing
H.-Y.‌‌ Wu was a reviewer for the International Conference‌ on Artificial Intelligence and‌ Cognitive Science
H.-Y. Wu‌‌ was a reviewer for the ICCV workshop on‌ Computer Vision for Metaverse‌ (CV4Metaverse)

11.1.2 Journal

Reviewer‌‌ - reviewing activities

H.-Y. Wu was a reviewer‌ for Multimedia Tools and‌ Applications
H.-Y. Wu was‌‌ a reviewer for Elsevier Social Sciences and Humanities‌
H.-Y. Wu was a‌ reviewer for Elsevier Computer‌‌ & Graphics

11.1.3 Invited‌ talks

B. Cessac gave an invited talk in‌ the Laconeu summer school.

11.1.4 Scientific expertise‌

H.-Y. Wu is the Handitechlab Inria project correspondent‌ for the Inria Centre at Univerité Côte d'Azur.‌

11.1.5 Research administration

B. Cessac was a member‌ of the Comité Scientifique de l'Institut Neuromod.
B.‌ Cessac is a member of the Bureau du‌ Comité des Equipes Projets.
B. Cessac was a‌ member of the Jury CRCN Inria 2025.
H.-Y.‌ Wu is a member of the Comité de‌ Suivi des Doctorants, Inria.

11.2 Teaching - Supervision‌ - Juries - Educational and pedagogical outreach

Master‌ 1: B. Cessac (24 hours, lecture) Introduction to‌ Modeling in Neuroscience, master Mod4NeuCog, Univ. Côte d'Azur,‌ France.
Master 1: L. Piovano (9 hours, tutorials)‌ Introduction to Modeling in Neuroscience, master Mod4NeuCog, Univ.‌ Côte d'Azur, France.
License 1: E. Petit (36‌ hours TD), Bases de l'informatique en python, License‌ math/info, Univ. Côte d'Azur, France.
Licence1: F. Franco‌ Gallo (36 hours, TP) Bases de l’informatique 1,‌ Licence Informatique, Univ. Côte d'Azur, France.
License 2:‌ J. Emonet (22 hours), Introduction à l'informatique, License‌ SV, Univ. Côte d'Azur, France.
License 3: J.‌ Emonet (16 hours), Programmation python et environnement linux,‌ License BIM, Univ. Côte d'Azur, France.
License 3:‌ J. Emonet (20 hours), Biostatistiques, License SV, Univ.‌ Côte d'Azur, France.
Master 2: H.-Y. Wu (7‌ hours, CM), Multimodal Interaction Techniques, Univ. Côte d'Azur,‌ France.
Master 1: H.-Y. Wu (12 hours, CM),‌ Creating Virtual Worlds, Univ. Côte d'Azur, France.
Master‌ 1: H.-Y. Wu (4 hours, CM), Introduction to‌ Scientific Research, DS4H, Univ. Côte d'Azur, France.
Master‌ 1: H.-Y. Wu (8 hours, CM), Introduction to‌ Scientific Research, Polytech, Univ. Côte d'Azur, France.
Master‌ 1: P. Sharma (30 hours, TD), Creating Virtual‌ Worlds, Univ. Côte d'Azur, France.

11.2.1 Supervision

B.‌ Cessac supervised the PhD of Erwan Petit, "Modeling‌ activity waves in the retina". Ended in October‌ 2025.
B. Cessac supervises the PhD of Laura‌ Piovano, "The roles of inhibition and adaptation in‌ retinal motion processing", started in October 2025.
P.‌ Kornprobst co-supervised (with D. Mazauric) the PhD of‌ Sebastian Gallardo, "Rethinking Newspaper Production: Optimizing Workflows with‌ AI". CIFRE contract with the company Demain un‌ Autre Jour (Toulouse).
P. Kornprobst and H.-Y. Wu‌ co-supervised the PhD of Johanna Delachambre, "Virtual Reality‌ to support social interaction in people with Age-Related‌ Macular Degeneration" 32; Defended on December 17.‌
H.-Y. Wu co-supervises the PhD of Franz Franco‌ Gallo with Lucile Sassatelli on "Modeling 6DoF Navigation‌ and the Impact of Low Vision in Immersive‌ VR Contexts"
H.-Y. Wu co-supervises the PhD of‌ Pauline Devictor with Marco Winckler on "Empathetic storytelling‌ in interactive extended reality"
H.-Y. Wu co-supervises the‌ PhD of Clément Quéré with Marco Winckler and‌ Aline Menin on "Contribution to extended reality for‌ visual exploration of spatio-temporal data"
H.-Y. Wu co-supervises‌ the PhD of Julie Tores with Lucile Sassatelli‌ and Frédéric Precioso on "Deep Learning to detect‌ objectification in movies"
B. Cessac supervised the M1 internship (Master Mod4NeuCog, Nice)‌ of Mranmay Sheetty,"To which‌ extent do artificial neural‌‌ networks capture dynamic retinal computation ?"
B. Cessac‌ co-supervised the M2 internship‌ (Master Mod4NeuCog, Nice) of‌‌ Mranmay Sheetty with L. Perrinet,"Unsupervised extraction of spiking‌ motifs in bi-photon data‌ of the mouse cerebral‌‌ cortex"
B. Cessac supervised the M2 internship (Magíster‌ en Ciencias de la‌ Ingeniería Informátic, Valparaiso) of‌‌ A. Navarro "Computational Approaches in Neuroscience for Aging‌ and Retinal Neuro Degeneration"‌
H.-Y. Wu co-supervised de‌‌ M2 internship of Wei-Tung Pan with Stephen Ramanoël‌ on "Multimodal Performance Analysis‌ of Spatial Navigation Tasks‌‌ in Virtual Reality"
H.-Y. Wu supervises the postdoc‌ of Paritosh Sharma on‌ "Deep generative approaches for‌‌ personalized training in virtual reality"

11.2.2 Juries

B.‌ Cessac was a member‌ of the thesis committee‌‌ (Comité de Suivi Individuel, CSI) of Anastasiia Maslianitsyna‌ (Institut de la Vision,‌ Paris).
P. Kornprobst was‌‌ a member of the PhD defense jury of‌ Eole Lapeyre, entitled “Adapting‌ to eccentric reading: visual‌‌ and psycholinguistic constraints under simulated and pathological central‌ visual field loss” (Aix-Marseille‌ Université), supervised by A.‌‌ Calabrèse.
P. Kornprobst was a member of the‌ CSI of Hussein Ammar,‌ PhD candidate at Université‌‌ de Nantes / CHU Nantes, working on the‌ evaluation of functional vision‌ in virtual reality.
P.‌‌ Kornprobst was a member of the CSI of‌ Tomas de Udaeta, PhD‌ candidate in computational neuroscience,‌‌ co-supervised by B. R. Cottereau and T. Masquelier.‌
P. Kornprobst was a‌ member of the CSI‌‌ of Matthis Dallain, PhD candidate supervised by B.‌ Miramond and L. Perrinet.‌
H.-Y. Wu was a‌‌ member of the CSI of Kevin Galery (Univ.‌ Côte d'Azur, École doctorale‌ Sciences de la Vie‌‌ et de la Santé, ED SVS).
H.-Y. Wu‌ was a member of‌ the CSI of Yujie‌‌ Huang (Université de Nantes).
H.-Y. Wu was a‌ member of the PhD‌ defense jury of Sophie‌‌ Villenave (École Centrale de Lyon) supervised by Guillaume‌ Lavoué and Pierre Raimbaud.‌

11.2.3 Educational and pedagogical‌‌ outreach

H.-Y. Wu was as a supervisor for‌ the “Rendez-vous des jeunes‌ mathématiciennes et informaticiennes” organized‌‌ by Terra Numerica in collaboration with Centre International‌ de Valbonne (CIV) to‌ share her research career‌‌ and guide students to carry out mini research‌ projects. A total of‌ 25 female students from‌‌ 2nd and 1ère attended.

12 Scientific production

12.1‌ Major publications

1 article‌Best paperE.Eric‌‌ Castet, J.Jérémy Termoz-Masson, S.Sebastian‌ Vizcay, J.Johanna‌ Delachambre, V.Vasiliki‌‌ Myrodia, C.Carlos Aguilar, F.Frédéric‌ Matonti and P.Pierre‌ Kornprobst. PTVR –‌‌ A software in Python to make virtual reality‌ experiments easier to build‌ and more reproducible.‌‌Journal of Vision24April 2024HAL DOI‌back to text
2‌ articleB.Bruno Cessac‌‌, I.Ignacio Ampuero and R.Rodrigo Cofré‌. Linear response for‌ spiking neuronal networks with‌‌ unbounded memory.Entropy232L'institution a‌ financé les frais de‌ publication pour que cet‌‌ article soit en libre‌ accèsFebruary 2021, 155HAL DOI
3‌ inproceedingsJ.Johanna Delachambre, H.-Y.Hui-Yin Wu‌, S.Sebastian Vizcay, M.Monica Di‌ Meo, F.Frédérique Lagniez, C.Christine‌ Morfin-Bourlat, S.Stéphanie Baillif and P.Pierre‌ Kornprobst. AMD Journee: A Patient Co-designed VR‌ Experience to Raise Awareness Towards the Impact of‌ AMD on Social Interactions.IMX 2024 -‌ ACM International Conference on Interactive Media ExperiencesStockholm,‌ SwedenJune 2024HALDOI back to text‌
4 articleS.Simone Ebert, T.Thomas‌ Buffet, B.B.Semihcan Sermet, O.Olivier‌ Marre and B.Bruno Cessac. Temporal pattern‌ recognition in retinal ganglion cells is mediated by‌ dynamical inhibitory synapses.Nature Communications151‌July 2024, 6118HAL DOI
5 article‌O.Olivier Faugeras, J.Jonathan Touboul and‌ B.Bruno Cessac. A constructive mean field‌ analysis of multi population neural networks with random‌ synaptic weights and stochastic inputs.Frontiers in‌ Computational Neuroscience312009, URL: http://arxiv.org/abs/0808.1113‌DOI
6 inproceedingsF. F.Franz Franco Gallo‌. DiVR: incorporating context from diverse VR scenes‌ for human trajectory prediction.CV4Metaverse workshop -‌ 3rd Computer Vision for Metaverse Workshop / Co-located‌ at ECCV 2024 - European Conference on Computer‌ VisionMilano, ItalySeptember 2024HAL back to‌ text back to text
7 inproceedingsF. F.‌Franz Franco Gallo, H.-Y.Hui-Yin Wu and‌ L.Lucile Sassatelli. Human Trajectory Forecasting in‌ 3D Environments: Navigating Complexity under Low Vision.‌ACM Digital LibraryMMVE 2024 - ACM Multimedia‌ Systems Workshop on IMmersive Mixed and Virtual Environment‌ SystemsMMVE '24: Proceedings of the 16th International‌ Workshop on Immersive Mixed and Virtual Environment Systems‌Bari, ItalyACMApril 2024, 57-63HAL‌DOI back to textback to text
8‌ articleD.Dora Matzakos-Karvouniari, L.Lionel Gil‌, E.Elaine Orendorff, O.Olivier Marre‌, S.Serge Picaud and B.Bruno Cessac‌. A biophysical model explains the spontaneous bursting‌ behavior in the developing retina.Scientific Reports‌91December 2019, 1-23HAL DOI‌
9 articleN. V.N. V. Kartheek Medathati‌, H.Heiko Neumann, G. S.Guillaume‌ S. Masson and P.Pierre Kornprobst. Bio-Inspired‌ Computer Vision: Towards a Synergistic Approach of Artificial‌ and Biological Vision.Computer Vision and Image‌ Understanding (CVIU)April 2016HAL DOI
10 article‌J.Jérémie Naudé, B.Bruno Cessac,‌ H.Hugues Berry and B.Bruno Delord.‌ Effects of Cellular Homeostatic Intrinsic Plasticity on Dynamical‌ and Computational Properties of Biological Recurrent Neural Networks‌.Journal of Neuroscience33382013,‌ 15032-15043HAL DOI
11 techreportD.Daniela Pamplona‌, G.Gerrit Hilgen, M. H.Matthias‌ H. Hennig, B.Bruno Cessac, E.‌Evelyne Sernagor and P.Pierre Kornprobst. Large‌ visual neuron assemblies receptive fields estimation using a‌ super-resolution approach.RR-9383Inria - Sophia antipolisDecember 2020HAL
12‌ articleJ.James Rankin‌, A. I.Andrew‌‌ I. Meso, G. S.Guillaume S. Masson‌, O.Olivier Faugeras‌ and P.Pierre Kornprobst‌‌. Bifurcation Study of a Neural Fields Competition‌ Model with an Application‌ to Perceptual Switching in‌‌ Motion Integration.Journal of Computational Neuroscience36‌22014, 193–213‌HAL
13 inproceedingsF.‌‌ A.Florent Alain Sauveur Robert, H.-Y.Hui-Yin‌ Wu, L.Lucile‌ Sassatelli, S.Stephen‌‌ Ramanoel, A.Auriane Gros and M.Marco‌ Winckler. An Integrated‌ Framework for Understanding Multimodal‌‌ Embodied Experiences in Interactive Virtual Reality.2023‌ IMX - ACM International‌ Conference on Interactive Media‌‌ ExperiencesNantes, FranceJune 2023, https://dl.acm.org/conference/imxHAL‌DOI back to text‌back to text
14‌‌ inproceedingsF. A.Florent Alain Sauveur Robert,‌ H.-Y.Hui-Yin Wu,‌ L.Lucile Sassatelli and‌‌ M.Marco Winckler. Task-based methodology to characterise‌ immersive user experience with‌ multivariate data.IEEE‌‌ VR 2024 - 31st IEEE conference on virtual‌ reality and 3D user‌ interfacesOrlando (FL), United‌‌ StatesMarch 2024HALback to text back‌ to text
15 article‌S.Selma Souihel and‌‌ B.Bruno Cessac. On the potential role‌ of lateral connectivity in‌ retinal anticipation.Journal‌‌ of Mathematical Neuroscience11January 2021HAL DOI‌
16 articleA.Adrien‌ Wohrer and P.Pierre‌‌ Kornprobst. Virtual Retina : A biological retina‌ model and simulator, with‌ contrast gain control.‌‌Journal of Computational Neuroscience262DOI 10.1007/s10827-008-0108-4‌We propose a new‌ retina simulation software, called‌‌ Virtual Retina, which transforms a video into spike‌ trains. Our goal is‌ twofold: Allow large scale‌‌ simulations (up to 100,000 neurons) in reasonable processing‌ times and keep a‌ strong biological plausibility, taking‌‌ into account implementation constraints. The underlying model includes‌ a linear model of‌ filtering in the Outer‌‌ Plexiform Layer, a shunting feedback at the level‌ of bipolar cells accounting‌ for rapid contrast gain‌‌ control, and a spike generation process modeling ganglion‌ cells. We prove the‌ pertinence of our software‌‌ by reproducing several experimental measurements from single ganglion‌ cells such as cat‌ X and Y cells.‌‌ This software will be an evolutionary tool for‌ neuroscientists that need realistic‌ large-scale input spike trains‌‌ in subsequent treatments, and for educational purposes.2009‌, 219
17 article‌H.-Y.Hui-Yin Wu,‌‌ A.Aurélie Calabrèse and P.Pierre Kornprobst.‌ Towards Accessible News Reading‌ Design in Virtual Reality‌‌ for Low Vision.Multimedia Tools and Applications‌May 2021HAL back‌ to text
18 article‌‌H.-Y.Hui-Yin Wu, F. A.Florent Alain‌ Sauveur Robert, T.‌Théo Fafet, B.‌‌Brice Graulier, B.Barthélemy Passin-Cauneau, L.‌Lucile Sassatelli and M.‌Marco Winckler. Designing‌‌ Guided User Tasks in VR Embodied Experiences.‌Proceedings of the ACM‌ on Human-Computer Interaction 6‌‌1582022, 1–24HAL DOI back to‌ text

12.2 Publications of‌ the year

International journals‌‌

19 articleB.Bruno‌ Cessac, E.Erwan Demairy, J.Jérôme‌ Emonet, E.Evgenia Kartsaki, T.Thibaud‌ Kloczko, C.Côme Le Breton, N.‌Nicolas Niclausse, S.Selma Souihel, J.-L.‌Jean-Luc Szpyrka and J.Julien Wintz. Macular:‌ a multi-scale simulation platform for the retina and‌ the primary visual system.Frontiers in Computational‌ NeuroscienceOctober 2025. In press. HAL back‌ to text back to text
20 articleJ.‌Jérôme Emonet, S.Selma Souihel, M.‌Matteo Di Volo, A.Alain Destexhe,‌ F.Frédéric Chavane and B.Bruno Cessac.‌ A chimera model for motion anticipation in the‌ retina and the primary visual cortex.Neural‌ Computation37112025, 1925-1974HAL DOI‌back to text back to text back to‌ text back to textback to text
21‌ articleH.-Y.Hui-Yin Wu, F. A.Florent‌ Alain Sauveur Robert, F. F.Franz Franco‌ Gallo, K.Kateryna Pirkovets, C.Clément‌ Quere, J.Johanna Delachambre, S.Stephen‌ Ramanoël, A.Auriane Gros, M.Marco‌ Winckler, L.Lucile Sassatelli, M.Meggy‌ Hayotte, A.Aline Menin and P.Pierre‌ Kornprobst. Exploring, walking, and interacting in virtual‌ reality with simulated low vision: a living contextual‌ dataset.Scientific Data 12330February 2025‌, 14HAL back to text back to‌ text back to text

Invited conferences

22 inproceedings‌S.Sebastian Gallardo, A.Aurélie Calabrèse,‌ M.Monica Di Meo, F.Frédérique Lagniez‌, C.Christine Morfin-Bourlat, S.Stéphanie Baillif‌, H.-Y.Hui-Yin Wu, D.Dorian Mazauric‌ and P.Pierre Kornprobst. Reading magnified newspapers‌ versus newspapers magnification: A new method to address‌ the local/global navigation problem on digital devices.‌The 15th international Conference on Low Vision Research‌ and RehabilitationFlorence, ItalySeptember 2025HAL back‌ to text back to text

International peer-reviewed conferences‌

23 inproceedingsE.Elisa Ancarani, J.Julie‌ Tores, R.Rémy Sun, L.Lucile‌ Sassatelli, H.-Y.Hui-Yin Wu and F.Frederic‌ Precioso. Leveraging Concept Annotations for Trustworthy Multimodal‌ Video Interpretation through Modality Specialization.MM '25:The‌ 33rd ACM International Conference on MultimediaDublin Ireland,‌ FranceACM; ACMOctober 2025, 11-19HAL‌DOI
24 inproceedingsJ.Johanna Delachambre, H.-Y.‌Hui-Yin Wu, P.Pierre Kornprobst, M.‌Monica Di Meo, F.Frédérique Lagniez,‌ C.Christine Morfin-Bourlat, S.Stéphanie Baillif and‌ E.Eric Castet. An asymmetric VR system‌ to configure and practice low-vision aids for social‌ interactions in clinical settings.IEEE VR 2025‌ - 32nd IEEE Conference on Virtual Reality and‌ 3D User InterfacesSaint-Malo, FranceMarch 2025HAL‌back to text back to text
25 inproceedings‌C.Clément Quere, A.Aline Menin,‌ E.Eliezer Bernart, C.Carla Maria Dal‌ Sasso Freitas, L.Luciana Nedel, P.‌Paulo Moura, H.-Y.Hui-Yin Wu and M.Marco Winckler. WristNotes:‌ Detachable Menu for Annotations‌ in Immersive Environments.‌‌20th IFIP TC13 International Conference on Human-Computer Interaction‌ (INTERACT 2025)Belo Horizonte,‌ Minas Gerais, BrazilSeptember‌‌ 2025HAL
26 inproceedingsC.Clément Quere,‌ A.Aline Menin,‌ H.-Y.Hui-Yin Wu,‌‌ P.Paulo Moura and M.Marco Winckler.‌ INSPIRE : An INcremental,‌ SPatio-temporal and Immersive approach‌‌ for Representing and Exploring mobility data.ACM‌ digital libraryCHItaly 2025‌ - 16th Biannual Conference‌‌ of the Italian SIGCHI ChapterSalerno, ItalyOctober‌ 2025HAL DOI
27‌ inproceedingsF. A.Florent‌‌ Alain Sauveur Robert, H.-Y.Hui-Yin Wu,‌ L.Lucile Sassatelli and‌ M.Marco Winckler.‌‌ The Triangle of Misunderstanding in Interactive Virtual Narratives:‌ Gulfs Between System, Designers‌ and Players.CHI‌‌ 2025 - CHI Conference on Human Factors in‌ Computing SystemsYokohama, Japan‌April 2025, 1‌‌ - 16HAL DOIback to text
28‌ inproceedingsJ.Julie Tores‌, E.Elisa Ancarani‌‌, R.Rémy Sun, L.Lucile Sassatelli‌, H.-Y.Hui-Yin Wu‌ and F.Frederic Precioso‌‌. Re-examining Concept-based Explainable Models for Multimodal Interpretative‌ Tasks.ACM digital‌ libraryMM 2025 -‌‌ 33rd ACM International Conference on MultimediaMM '25:‌ Proceedings of the 33rd‌ ACM International Conference on‌‌ MultimediaDublin, IrelandACMOctober 2025, 12437-12445‌HAL DOI
29 inproceedings‌H.-Y.H.-Y. Wu and‌‌ S.Stéphanie Le Briz-Orgeur. Theatron VR AI:‌ bringing audiences (back) to‌ the theater through virtual‌‌ reality and the art of improvisation.JIT‌ 2024 - Journées d’Informatique‌ Théâtrale (3rd session)Avignon,‌‌ FranceApril 2025HAL

Conferences without proceedings

30‌ inproceedingsC.Camille Bordeau‌, C.Célia Passerel‌‌, C.Carlos Aguilar, I.Iliana Huyet‌, C.Caroline Topart‌, F.François Devin‌‌, F.Frédéric Matonti, P.Pierre Kornprobst‌ and E.Eric Castet‌. Central field loss‌‌ patients' ability to select targets with head-pointing using‌ virtual reality: An exploratory‌ psychophysical study.The‌‌ 15th international Conference on Low Vision Research and‌ RehabilitationFlorence, ItalySeptember‌ 2025HAL back to‌‌ text back to text
31 inproceedingsS.Sebastian‌ Gallardo, M.-C.María-Cristina‌ Riff, D.Dorian‌‌ Mazauric and P.Pierre Kornprobst. Evolutionary Newspaper‌ Magnification with Preserved Entry‌ Points.SophI.A Summit‌‌Sophia Antipolis (06), FranceNovember 2025HAL back‌ to text

Doctoral dissertations‌ and habilitation theses

32‌‌ thesisJ.Johanna Delachambre. Virtual Reality to‌ Support Social Interaction in‌ People with Age-Related Macular‌‌ Degeneration.Université côte d'azurDecember 2025HAL‌back to text

Reports‌ & preprints

33 misc‌‌E.Elisa Ancarani, J.Julie Tores,‌ L.Lucile Sassatelli,‌ R.Rémy Sun,‌‌ H.-Y.Hui-Yin Wu and F.Frédéric Precioso.‌ Leveraging Multimodal Explanatory Annotations‌ For Video Interpretation With‌‌ Modality Specific Dataset.April 2025HAL
34‌ reportJ.Johanna Delachambre‌, H.-Y.Hui-Yin Wu‌‌, M. D.Monica Di Meo, F.‌Frédérique Lagniez, C.‌Christine Morfin-Bourlat, S.‌‌Stéphanie Baillif, A.‌Aurélie Calabrèse, E.Eric Castet and P.‌Pierre Kornprobst. Select and Augment Segmented Items‌ (SASI): An Item-based Magnification Approach for Dynamic Face‌ Recognition for People with Central Vision Loss.‌RR-9597Centre Inria d'Université Côte d'AzurSeptember 2025‌HAL back to text
35 miscS.Simone‌ Ebert and B.Bruno Cessac. Distinct inhibitory‌ connectivity motifs trigger distinct forms of anticipation in‌ the retinal network.August 2025HAL back‌ to text
36 miscJ.Jérôme Emonet and‌ B.Bruno Cessac. The refresh rate of‌ overhead projectors may affect the perception of fast‌ moving objects: a modelling study.April 2025‌HAL
37 miscJ.Julie Tores, E.‌Elisa Ancarani, L.Lucile Sassatelli, H.-Y.‌Hui-Yin Wu, C.Clement Bergman, L.‌Lea Andolfi, V.Victor Ecrement, R.‌Remy Sun, F.Frederic Precioso, T.‌Thierry Devars, M.Magali Guaresi, V.‌Virginie Julliard and S.Sarah Lecossais. MObyGaze:‌ a film dataset of multimodal objectification densely annotated‌ by experts.May 2025HAL

Other scientific‌ publications

38 misc B.Bruno Cessac. How‌ does lateral inhibition shape the response to moving‌ stimuli ? Valparaiso (Chile), Chile January 2025 HAL‌
39 miscB.Bruno Cessac. The early‌ visual system from a dynamical system perspective: Main‌ course.Valparaiso (Chile), ChileJanuary 2025HAL‌
40 miscB.Bruno Cessac. The retinal‌ network from a dynamical system perspective.Tuebingen,‌ GermanyJanuary 2026HAL

Educational activities

41 unpublished‌B.Bruno Cessac. The early visual system‌ from a dynamical system perspective: Tutorial.January‌ 2025, DoctoralChileHAL

12.3 Cited publications‌

42 articleG.Giacomo Benvenuti, S.Sandrine‌ Chemla, A.Arjan Boonman, L.Laurent‌ Perrinet, G. S.Guillaume S Masson and‌ F.Frédéric Chavane. Anticipatory responses along motion‌ trajectories in awake monkey area V1.bioRxiv‌2020, URL: https://www.biorxiv.org/content/early/2020/03/29/2020.03.26.010017DOI back to text‌back to text
43 articleM.M.J. Berry‌, I.I.H. Brivanlou, T.T.A. Jordan‌ and M.M. Meister. Anticipation of moving‌ stimuli by the retina.Nature3986725‌1999, 334---338back to text
44 book‌J.J. Besharse and D.D. Bok.‌ The Retina and its Disorders.Elsevier Science‌2011back to text
45 articleA.A.‌ Bhowmick and S. M.S. M. Hazarika.‌ An insight into assistive technology for the visually‌ impaired and blind people: state-of- the-art and future‌ trends.J Multimodal User Interfaces112017‌, 149-172DOI back to text
46 article‌J. C.J. C. Brown, J. E.‌J. E. Goldstein, T. L.T. L.‌ Chan, M.Massof R., P.P.‌ Ramulu and L. V.Low Vision Research Network‌ Study Group. Characterizing functional complaints in patients‌ seeking outpatient low-vision services in the United States‌.Ophthalmology12182014, 1655-62DOIback to text
47‌ articleA.Aurélie Calabrèse‌, V.Vincent Fournet‌‌, S.Séverine Dours, F.Frédéric Matonti‌, E.Eric Castet‌ and P.Pierre Kornprobst‌‌. A New Vessel-Based Method to Estimate Automatically‌ the Position of the‌ Nonfunctional Fovea on Altered‌‌ Retinography From Maculopathies.Translational vision science &‌ technology12July 2023‌HAL DOI back to‌‌ text
48 articleB.Bruno Cessac and D.‌Dora Matzakou-Karvouniari. The‌ non linear dynamics of‌‌ retinal waves.Physica D: Nonlinear PhenomenaNovember‌ 2022HAL DOI back‌ to text
49 article‌‌S. T.S. T. L. Chung. Cortical‌ Reorganization after Long-Term Adaptation‌ to Retinal Lesions in‌‌ Humans.J Neurosci.33462013,‌ 18080--18086DOI back to‌ text
50 articleJ.‌‌ L.J. L. Fontenot, M. D.M.‌ D. Bona, M.‌ A.M. A. Kaleem‌‌, W. M.W. M. McLaughlin, A.‌ R.A. R. Morse‌, T. L.T.‌‌ L. Schwartz, J. D.J. D. Shepherd‌ and M. L.M.‌ L. Jackson. Vision‌‌ Rehabilitation Preferred Practice Pattern.Ophthalmology1251‌2018, 228-278DOI‌back to text
51‌‌ unpublishedS.Sebastian Gallardo, M. C.Mar\'ia‌ Cristina Riff, D.‌Dorian Mazauric and P.‌‌Pierre Kornprobst. Newspaper Magnification with Preserved Entry‌ Points.September 2023‌, working paper or‌‌ preprintHAL back to text back to text‌
52 articleD.D.‌ Jancke, W.W.‌‌ Erlaghen, G.G. Schöner and H.H.R.‌ Dinse. Shorter latencies‌ for motion trajectories than‌‌ for flashes in population responses of primary visual‌ cortex.Journal of‌ Physiology5562004,‌‌ 971--982back to text
53 phdthesisE.Evgenia‌ Kartsaki. How specific‌ classes of retinal cells‌‌ contribute to vision : a computational model.‌Université Côte d'Azur ;‌ Newcastle University (Newcastle upon‌‌ Tyne, Royaume-Uni)March 2022HAL back to text‌
54 articleG. E.‌G. E. Legge and‌‌ S. T.S. T. L. Chung. Low‌ Vision and Plasticity: Implications‌ for Rehabilitation.Annu‌‌ Rev Vis Sci.22016, 321--343DOI‌back to text
55‌ bookD. A.Donald‌‌ A Norman. The psychology of everyday things.‌.Basic books1988‌back to text back‌‌ to text
56 articleG.G. Rees,‌ C. L.C. L.‌ Saw, E. L.‌‌E. L. Lamoureux and J. E.J. E.‌ Keeffe. Self-management programs‌ for adults with low‌‌ vision: needs and challenges.Patient Educ Couns‌691-3December 2007‌, 39-46DOI back‌‌ to text
57 articleS.Selma Souihel and‌ B.Bruno Cessac.‌ On the potential role‌‌ of lateral connectivity in retinal anticipation.Journal‌ of Mathematical Neuroscience11‌January 2021HAL DOI‌‌back to text
58 articleW.W.L. Wong‌, X.X. Su‌, X.X. Li‌‌, C.C.M. Cheung, R.R. Klein‌, C.C.Y. Cheng‌ and T.T.Y. Wong‌‌. Global prevalence of‌ age-related macular degeneration and diseas,e burden projection for‌ 2020 and 2040: a systematic rev,iew and meta-analysis‌.Lancet Glob Health22February 2014‌, 106-116DOI back to text
59 article‌H.-Y.Hui-Yin Wu, A.Aurélie Calabrèse and‌ P.Pierre Kornprobst. An Open Virtual Reality‌ Toolbox for Accessible News Reading.ERCIM News‌130July 2022HALback to text
60‌ incollectionH.-Y.Hui-Yin Wu, J.Johanna Delachambre‌, L.Lucile Sassatelli and M.Marco Winckler‌. Through the Eyes of Women in Engineering‌.Texts of DiscomfortCarnegie Mellon University: ETC‌ PressNovember 2021, 387 - 414HAL‌DOI back to text
61 inproceedingsH.-Y.Hui-Yin‌ Wu, L.Luan Nguyen, Y.Yoldoz‌ Tabei and L.Lucile Sassatelli. Evaluation of‌ deep pose detectors for automatic analysis of film‌ style.10th Eurographics Workshop on Intelligent Cinematography‌ and EditingReims, FranceAssociation for Computing Machinery‌ (ACM)April 2022HALback to text

2025Activity report​​​‌Project-TeamBIOVISION

Keywords

1 Team​‌﻿﻿ members, visitors, external collaborators​​﻿﻿

Research Scientists

Post-Doctoral Fellow

PhD Students​‌﻿﻿

Technical​​﻿﻿ Staff

Interns​​﻿﻿ and Apprentices

Administrative Assistant

External Collaborators

2 Overall objectives​​​‌

3​​​‌ Research program

3.1 Axis﻿​﻿﻿ 1 - Modeling the​‌﻿﻿ retina and the primary​​﻿﻿ visual system.

3.2 Axis​​﻿﻿ 2 - Diagnosis, rehabilitation,​​​‌ and low-vision aids.

3.3 Axis 3 -﻿﻿﻿‌ Visual media analysis and﻿‌​‌ creation.

4 Application domains​​​‌

4.1 Applications of low-vision﻿​﻿﻿ studies

4.2​‌﻿﻿ Applications of vision modeling​​﻿﻿ studies

4.3 Applications​​​‌ of visual media analysis﻿﻿﻿‌ and creation

5 Social and environmental﻿﻿﻿‌ responsibility

6﻿‌​‌ Highlights of the year﻿​​﻿

6.1 Awards

7 Latest software﻿‌​‌ developments, platforms, open data﻿​​﻿

7.1 Latest software developments​​​‌

7.1.1 Macular

7.1.2 InREAD​​﻿﻿

7.1.3 GUsT-3D

7.1.4 PTVR﻿‌​‌

7.1.5​​​‌ Tatoovi

7.2﻿​﻿﻿ New platforms

7.2.1​​​‌ CREATTIVE3D platform for navigation﻿​﻿﻿ studies in VR

7.3 Open data﻿​​﻿

CREATTIVE3D multimodal dataset of​​​‌ user behavior in virtual﻿﻿﻿‌ reality

8 New﻿﻿﻿‌ results

8.1 Modeling the﻿﻿﻿‌ retina and the primary﻿‌​‌ visual system

8.1.1 Distinct﻿​​﻿ inhibitory connectivity motifs trigger​​​‌ distinct forms of anticipation﻿﻿﻿‌ in the retinal network﻿‌​‌

8.1.2​‌﻿﻿ Macular: a multi-scale simulation​​﻿﻿ platform for the retina​​​‌ and the primary visual﻿​﻿﻿ system

8.1.3﻿​﻿﻿ A chimera model for​‌﻿﻿ motion anticipation in the​​﻿﻿ retina and the primary​​​‌ visual cortex

8.1.4 The﻿﻿﻿‌ refresh rate of overhead﻿‌​‌ projectors may affect the﻿​​﻿ perception of fast moving​​​‌ objects: a modeling study﻿﻿﻿‌

8.2 Diagnosis,​​​‌ rehabilitation, and low-vision aids﻿​﻿﻿

8.2.1 Central field loss​‌﻿﻿ patients' ability to select​​﻿﻿ targets with head-pointing using​​​‌ virtual reality: An exploratory﻿​﻿﻿ psychophysical study

8.2.2 An asymmetric VR​​​‌ system to configure and﻿﻿﻿‌ practice low-vision aids for﻿‌​‌ social interactions in clinical﻿​​﻿ settings

8.2.3 Select and Augment​​​‌ Segmented Items (SASI): An﻿​﻿﻿ item-based magnification approach for​‌﻿﻿ dynamic face recognition for​​﻿﻿ people with central vision​​​‌ loss

8.2.4 Reading magnified​​​‌ newspapers versus newspapers magnification:﻿﻿﻿‌ A new method to﻿‌​‌ address the local/global navigation﻿​​﻿ problem on digital devices​​​‌

8.3​​﻿﻿ Visual media analysis and​​​‌ creation

8.3.1 Exploring, walking,﻿​﻿﻿ and interacting in virtual​‌﻿﻿ reality with simulated low​​﻿﻿ vision: a living contextual​​​‌ dataset

8.3.2 The triangle﻿﻿﻿‌ of misunderstanding in interactive﻿‌​‌ virtual narratives: gulfs between﻿​​﻿ system, designers and players​​​‌

8.3.3 SILOVIS: SImulating LOw​​﻿﻿ Vision through Immersive Storytelling​​​‌

9 Bilateral contracts and﻿​​﻿ grants with industry

9.1​​​‌ Bilateral contracts with industry﻿﻿﻿‌

10 Partnerships and cooperations﻿​​﻿