6.1.1 AsymGait

• Name:
  Asymmetry index for clinical gait analysis based on depth images
• Keywords:
  Motion analysis, Kinect, Clinical analysis
• Scientific Description:
  The system uses depth images delivered by the Microsoft Kinect to retrieve the gait cycles first. To this end it is based on a analyzing the knees trajectories instead of the feet to obtain more robust gait event detection. Based on these cycles, the system computes a mean gait cycle model to decrease the effect of noise of the system. Asymmetry is then computed at each frame of the gait cycle as the spatial difference between the left and right parts of the body. This information is computed for each frame of the cycle.
• Functional Description:
  AsymGait is a software package that works with Microsoft Kinect data, especially depth images, in order to carry-out clinical gait analysis. First it identifies the main gait events using the depth information (footstrike, toe-off) to isolate gait cycles. Then it computes a continuous asymmetry index within the gait cycle. Asymmetry is viewed as a spatial difference between the two sides of the body.
• Contact:
  Franck Multon
• Participants:
  Edouard Auvinet, Franck Multon

6.1.2 Cinematic Viewpoint Generator

• Keyword:
  3D animation
• Functional Description:
  The software, developed as an API, provides a mean to automatically compute a collection of viewpoints over one or two specified geometric entities, in a given 3D scene, at a given time. These viewpoints satisfy classical cinematographic framing conventions and guidelines including different shot scales (from extreme long shot to extreme close-up), different shot angles (internal, external, parallel, apex), and different screen compositions (thirds,fifths, symmetric of di-symmetric). The viewpoints allow to cover the range of possible framings for the specified entities. The computation of such viewpoints relies on a database of framings that are dynamically adapted to the 3D scene by using a manifold parametric representation and guarantee the visibility of the specified entities. The set of viewpoints is also automatically annotated with cinematographic tags such as shot scales, angles, compositions, relative placement of entities, line of interest.
• Contact:
  Marc Christie
• Participants:
  Christophe Lino, Emmanuel Badier, Marc Christie
• Partners:
  Université d'Udine, Université de Nantes

6.1.3 CusToM

• Name:
  Customizable Toolbox for Musculoskeletal simulation
• Keywords:
  Biomechanics, Dynamic Analysis, Kinematics, Simulation, Mechanical multi-body systems
• Scientific Description:

  The present toolbox aims at performing a motion analysis thanks to an inverse dynamics method.

  Before performing motion analysis steps, a musculoskeletal model is generated. Its consists of, first, generating the desire anthropometric model thanks to models libraries. The generated model is then kinematical calibrated by using data of a motion capture. The inverse kinematics step, the inverse dynamics step and the muscle forces estimation step are then successively performed from motion capture and external forces data. Two folders and one script are available on the toolbox root. The Main script collects all the different functions of the motion analysis pipeline. The Functions folder contains all functions used in the toolbox. It is necessary to add this folder and all the subfolders to the Matlab path. The Problems folder is used to contain the different study. The user has to create one subfolder for each new study. Once a new musculoskeletal model is used, a new study is necessary. Different files will be automaticaly generated and saved in this folder. All files located on its root are related to the model and are valuable whatever the motion considered. A new folder will be added for each new motion capture. All files located on a folder are only related to this considered motion.

• Functional Description:
  Inverse kinematics Inverse dynamics Muscle forces estimation External forces prediction
• Publications:
  hal-02268958, hal-02088913, hal-02109407, hal-01904443, hal-02142288, hal-01988715, hal-01710990
• Contact:
  Charles Pontonnier
• Participants:
  Antoine Muller, Charles Pontonnier, Georges Dumont, Pierre Puchaud, Anthony Sorel, Claire Livet, Louise Demestre

6.1.4 Directors Lens Motion Builder

• Keywords:
  Previzualisation, Virtual camera, 3D animation
• Functional Description:
  Directors Lens Motion Builder is a software plugin for Autodesk's Motion Builder animation tool. This plugin features a novel workflow to rapidly prototype cinematographic sequences in a 3D scene, and is dedicated to the 3D animation and movie previsualization industries. The workflow integrates the automated computation of viewpoints (using the Cinematic Viewpoint Generator) to interactively explore different framings of the scene, proposes means to interactively control framings in the image space, and proposes a technique to automatically retarget a camera trajectory from one scene to another while enforcing visual properties. The tool also proposes to edit the cinematographic sequence and export the animation. The software can be linked to different virtual camera systems available on the market.
• Contact:
  Marc Christie
• Participants:
  Christophe Lino, Emmanuel Badier, Marc Christie
• Partner:
  Université de Rennes 1

6.1.5 Kimea

• Name:
  Kinect IMprovement for Egronomics Assessment
• Keywords:
  Biomechanics, Motion analysis, Kinect
• Scientific Description:
  Kimea consists in correcting skeleton data delivered by a Microsoft Kinect in an ergonomics purpose. Kimea is able to manage most of the occlultations that can occur in real working situation, on workstations. To this end, Kimea relies on a database of examples/poses organized as a graph, in order to replace unreliable body segments reconstruction by poses that have already been measured on real subject. The potential pose candidates are used in an optimization framework.
• Functional Description:
  Kimea gets Kinect data as input data (skeleton data) and correct most of measurement errors to carry-out ergonomic assessment at workstation.
• Publications:
  hal-01612939v1, hal-01393066v1, hal-01332716v1, hal-01332711v2, hal-01095084v1
• Contact:
  Franck Multon
• Participants:
  Franck Multon, Hubert Shum, Pierre Plantard
• Partner:
  Faurecia

6.1.6 Populate

• Keywords:
  Behavior modeling, Agent, Scheduling
• Scientific Description:

  The software provides the following functionalities:

  - A high level XML dialect that is dedicated to the description of agents activities in terms of tasks and sub activities that can be combined with different kind of operators: sequential, without order, interlaced. This dialect also enables the description of time and location constraints associated to tasks.

  - An XML dialect that enables the description of agent's personal characteristics.

  - An informed graph describes the topology of the environment as well as the locations where tasks can be performed. A bridge between TopoPlan and Populate has also been designed. It provides an automatic analysis of an informed 3D environment that is used to generate an informed graph compatible with Populate.

  - The generation of a valid task schedule based on the previously mentioned descriptions.

  With a good configuration of agents characteristics (based on statistics), we demonstrated that tasks schedules produced by Populate are representative of human ones. In conjunction with TopoPlan, it has been used to populate a district of Paris as well as imaginary cities with several thousands of pedestrians navigating in real time.

• Functional Description:
  Populate is a toolkit dedicated to task scheduling under time and space constraints in the field of behavioral animation. It is currently used to populate virtual cities with pedestrian performing different kind of activities implying travels between different locations. However the generic aspect of the algorithm and underlying representations enable its use in a wide range of applications that need to link activity, time and space. The main scheduling algorithm relies on the following inputs: an informed environment description, an activity an agent needs to perform and individual characteristics of this agent. The algorithm produces a valid task schedule compatible with time and spatial constraints imposed by the activity description and the environment. In this task schedule, time intervals relating to travel and task fulfillment are identified and locations where tasks should be performed are automatically selected.
• Contact:
  Fabrice Lamarche
• Participants:
  Carl-Johan Jorgensen, Fabrice Lamarche

6.1.7 PyNimation

• Keywords:
  Moving bodies, 3D animation, Synthetic human
• Scientific Description:
  PyNimation is a python-based open-source (AGPL) software for editing motion capture data which was initiated because of a lack of open-source software enabling to process different types of motion capture data in a unified way, which typically forces animation pipelines to rely on several commercial software. For instance, motions are captured with a software, retargeted using another one, then edited using a third one, etc. The goal of Pynimation is therefore to bridge the gap in the animation pipeline between motion capture software and final game engines, by handling in a unified way different types of motion capture data, providing standard and novel motion editing solutions, and exporting motion capture data to be compatible with common 3D game engines (e.g., Unity, Unreal). Its goal is also simultaneously to provide support to our research efforts in this area, and it is therefore used, maintained, and extended to progressively include novel motion editing features, as well as to integrate the results of our research projects. At a short term, our goal is to further extend its capabilities and to share it more largely with the animation/research community.
• Functional Description:

  PyNimation is a framework for editing, visualizing and studying skeletal 3D animations, it was more particularly designed to process motion capture data. It stems from the wish to utilize Python’s data science capabilities and ease of use for human motion research.

  In its version 1.0, Pynimation offers the following functionalities, which aim to evolve with the development of the tool : - Import / Export of FBX, BVH, and MVNX animation file formats - Access and modification of skeletal joint transformations, as well as a certain number of functionalities to manipulate these transformations - Basic features for human motion animation (under development, but including e.g. different methods of inverse kinematics, editing filters, etc.). - Interactive visualisation in OpenGL for animations and objects, including the possibility to animate skinned meshes

• URL:
  https://­gitlab.­inria.­fr/­lhoyet/­pynimation
• Authors:
  Ludovic Hoyet, Robin Adili, Benjamin Niay, Alberto Jovane
• Contact:
  Ludovic Hoyet

6.1.8 The Theater

• Keywords:
  3D animation, Interactive Scenarios
• Functional Description:
  The Theater is a software framework to develop interactive scenarios in virtual 3D environements. The framework provides means to author and orchestrate 3D character behaviors and simulate them in real-time. The tools provides a basis to build a range of 3D applications, from simple simulations with reactive behaviors, to complex storytelling applications including narrative mechanisms such as flashbacks.
• Contact:
  Marc Christie
• Participant:
  Marc Christie

6.2.1 Immerstar Platform

Participants: Georges Dumont [contact], Ronan Gaugne, Anthony Sorel, Richard Kulpa.

With the two platforms of virtual reality, Immersia) and Immermove Immermove, grouped under the name Immerstar, the team has access to high level scientific facilities. This equipment benefits the research teams of the center and has allowed them to extend their local, national and international collaborations. The Immerstar platform was granted by a Inria CPER funding for 2015-2019 that enabled important evolutions of the equipment. In 2016, the first technical evolutions have been decided and, in 2017, these evolutions have been implemented. On one side, for Immermove, the addition of a third face to the immersive space, and the extension of the Vicon tracking system have been realized and continued this year with 23 new cameras. And, on the second side, for Immersia, the installation of WQXGA laser projectors with augmented global resolution, of a new tracking system with higher frequency and of new computers for simulation and image generation in 2017. In 2018, a Scale One haptic device has been installed. It allows, as in the CPER proposal, one or two handed haptic feedback in the full space covered by Immersia and possibility of carrying the user. Based on these supports, in 2020, we participated to a PIA3-Equipex+ proposal. This proposal CONTINUUM involves 22 partners, has been succesfully evaluated and will be granted. The CONTINUUM project will create a collaborative research infrastructure of 30 platforms located throughout France, to advance interdisciplinary research based on interaction between computer science and the human and social sciences. Thanks to CONTINUUM, 37 research teams will develop cutting-edge research programs focusing on visualization, immersion, interaction and collaboration, as well as on human perception, cognition and behaviour in virtual/augmented reality, with potential impact on societal issues. CONTINUUM enables a paradigm shift in the way we perceive, interact, and collaborate with complex digital data and digital worlds by putting humans at the center of the data processing workflows. The project will empower scientists, engineers and industry users with a highly interconnected network of high-performance visualization and immersive platforms to observe, manipulate, understand and share digital data, real-time multi-scale simulations, and virtual or augmented experiences. All platforms will feature facilities for remote collaboration with other platforms, as well as mobile equipment that can be lent to users to facilitate onboarding. The kick-off meeting of continuum has been held in 2022, January the 14th. A global meeting was held in 2022, July the 5th and 6th.

7.2.1 Study on Automatic 3D Facial Caricaturization: From Rules to Deep Learning

Participants: Ludovic Hoyet, Franck Multon [contact], Nicolas Olivier.

Facial caricature is the art of drawing faces in an exaggerated way to convey emotions such as humor or sarcasm. Automatic caricaturization has been explored both in the 2D and 3D domain. In this work 28, we propose two novel approaches to automatically caricaturize input facial scans, filling gaps in the literature in terms of user-control, caricature style transfer, and exploring the use of deep learning for 3D mesh caricaturization. The first approach is a gradient-based differential deformation approach with data driven stylization (Figure 4). It is a combination of two deformation processes: facial curvature and proportions exaggeration. The second approach is a GAN for unpaired face-scan-to-3D-caricature translation. We leverage existing facial and caricature datasets, along with recent domain-to-domain translation methods and 3D convolutional operators, to learn to caricaturize 3D facial scans in an unsupervised way. To evaluate and compare these two novel approaches with the state of the art, we conducted the first user study of facial mesh caricaturization techniques, with 49 participants. It highlights the subjectivity of the caricature perception and the complementarity of the methods. Finally, we provide insights for automatically generating caricaturized 3D facial mesh.

7.2.2 AIP: Adversarial Interaction Priors for Multi-Agent Physics-based Character Control

Participants: Mohamed Younes, Franck Multon [contact], Richard Kulpa.

Simulated shadowboxing interactions between two physics-based characters.

We address the problem of controlling and simulating interactions between multiple physics-based characters, using short unlabeled motion clips 58. We propose Adversarial Interaction Priors (AIP), a multi-agents generative adversarial imitation learning (MAGAIL) approach, which extends recent deep reinforcement learning (RL) works aiming at imitating single character example motions. The main contribution of this work is to extend the idea of motion imitation of a single character to interaction imitation between multiple characters. Our method uses a control policy for each character to imitate interactive behaviors provided by short example motion clips, and associates a discriminator for each character, which is trained on actor-specific interactive motion clips. The discriminator returns interaction rewards that measure the similarity between generated behaviors and demonstrated ones in the reference motion clips. The policies and discriminators are trained in a multi-agent adversarial reinforcement learning procedure, to improve the quality of the behaviors generated by each agent. The initial results show the effectiveness of our method on the interactive task of shadowboxing between two fighters.

7.2.3 Interaction Fields: Intuitive Sketch-based Steering Behaviors for Crowd Simulation

Participants: Marc Christie, Adèle Colas, Ludovic Hoyet [contact], Anne-Hélène Olivier, Katja Zibrek.

The real-time simulation of human crowds has many applications. In a typical crowd simulation, each person (`agent') in the crowd moves towards a goal while adhering to local constraints. Many algorithms exist for specific local `steering' tasks such as collision avoidance or group behavior. However, these do not easily extend to completely new types of behavior, such as circling around another agent or hiding behind an obstacle. They also tend to focus purely on an agent's velocity without explicitly controlling its orientation. This work 14 presents a novel sketch-based method for modelling and simulating many steering behaviors for agents in a crowd (Figure 6). Central to this is the concept of an interaction field (IF): a vector field that describes the velocities or orientations that agents should use around a given `source' agent or obstacle. An IF can also change dynamically according to parameters, such as the walking speed of the source agent. IFs can be easily combined with other aspects of crowd simulation, such as collision avoidance. Using an implementation of IFs in a real-time crowd simulation framework, we demonstrate the capabilities of IFs in various scenarios. This includes game-like scenarios where the crowd responds to a user-controlled avatar. We also present an interactive tool that computes an IF based on input sketches. This IF editor lets users intuitively and quickly design new types of behavior, without the need for programming extra behavioral rules. We thoroughly evaluate the efficacy of the IF editor through a user study, which demonstrates that our method enables non-expert users to easily enrich any agent-based crowd simulation with new agent interactions. This work was performed in collaboration with Julien Pettré from the Virtus team.

7.2.4 Dynamic Combination of Crowd Steering Policies Based on Context

Participants: Ludovic Hoyet [contact].

Simulation of two agent flows crossing at 90º using three different motion strategies, and their respective score SQFS_{QF} according to a quality function (a) Agents are steered using the Power Law model (PL, blue). Note that some agents are dragged along a diagonal towards the up-right direction, hence deviating them from their goal. PL (SQF=67S_{QF}=67)

Simulation of two agent flows crossing at 90º using three different motion strategies, and their respective score SQFS_{QF} according to a quality function. (c) Using our approach, characters dynamically switch motion policy depending on their local context, hence overcoming the motion artifacts. In this example, characters use the PL model, but switch to RVO in the 90º crossing context. The agents' color encodes their current policy. Our dynamic adaptation results in an increase of the overall quality score, SQFS_{QF}. PL and RVO (SQF=92S_{QF}=92)

Simulation of two agent flows crossing at 90º using three different motion strategies, and their respective score SQFS_{QF} according to a quality function. (c) Using our approach, characters dynamically switch motion policy depending on their local context, hence overcoming the motion artifacts displayed in (a) and (b). In this example, characters use the PL model, but switch to RVO in the 90º crossing context. The agents' color encodes their current policy. Our dynamic adaptation results in an increase of the overall quality score, SQFS_{QF}.

Simulating crowds requires controlling a very large number of trajectories of characters and is usually performed using crowd steering algorithms. The question of choosing the right algorithm with the right parameter values is of crucial importance given the large impact on the quality of results. In this work 12, we study the performance of a number of steering policies (i.e., simulation algorithm and its parameters) in a variety of contexts, resorting to an existing quality function able to automatically evaluate simulation results. This analysis allows us to map contexts to the performance of steering policies. Based on this mapping, we demonstrate that distributing the best performing policies among characters improves the resulting simulations. Furthermore, we also propose a solution to dynamically adjust the policies, for each agent independently and while the simulation is running, based on the local context each agent is currently in. We demonstrate significant improvements of simulation results compared to previous work that would optimize parameters once for the whole simulation, or pick an optimized, but unique and static, policy for a given global simulation context (Figures 7, 8, 9 ). This work was performed in collaboration with Julien Pettré from the Virtus team.

7.2.5 Impact of Self-Contacts on Perceived Pose Equivalences

Participants: Franck Multon [contact], Jean Basset, Adnane Boukhayma, Ludovic Hoyet.

In 2018, we introduced the idea of context graph to capture the relationship between body parts surfaces and enhance the quality of the motion retargetting problem. Hence, it becomes possible to retarget the motion of a source character to a target one while preserving the topological relationship between body parts surfaces. However this approach implies to strictly satisfy distance constraints between body parts, whereas some of them could be relaxed to preserve naturalness. In 2019, we introduced a new paradigm based on transfering the shape instead of encoding the pose constraints to tackle this problem for isolated poses.

In 2020, we extended this approach to handle continuity in motion, and non-human characters. this idea resulted from the collaboration with the Morpheo Inria team in Grenoble, in the context of the IPL AVATAR project. The proposed approaches were based on a nonlinear optimization framework, which involved manually editing the constraints to satisfy, and required a huge amount of computation time.

In 2021, to achieve the deformation transfer, we proposed a neural encoder-decoder architecture where only identity information is encoded and where the decoder is conditioned on the pose. We use pose independent representations, such as isometry-invariant shape characteristics, to represent identity features. The model is depicted in Figure 10. Our model uses these features to supervise the prediction of offsets from the deformed pose to the result of the transfer. We show experimentally that our method outperforms state-of-the-art methods both quantitatively and qualitatively, and generalizes better to poses not seen during training. We also introduce a fine-tuning step that allows to obtain competitive results for extreme identities, and allows to transfer simple clothing.

Defining equivalences between poses of different human characters is an important problem for imitation research, human pose recognition and deformation transfer. However, pose equivalence is a subjective information that depends on context and on the morphology of the characters. A common hypothesis is that interactions between body surfaces, such as self-contacts, are important attributes of human poses, and are therefore consistently included in animation approaches aiming at retargeting human motions. However, some of these self-contacts are only present because of the morphology of the character and are not important to the pose, e.g. contacts between the upper arms and the torso during a standing A-pose. In 37, we conduct a first study towards the goal of understanding the impact of self-contacts between body surfaces on perceived pose equivalences. More specifically, we focus on contacts between the arms or hands and the upper body, which are frequent in everyday human poses. We conduct a study where we present to observers two models of a character mimicking the pose of a source character, one with the same self-contacts as the source, and one with one self-contact removed, and ask observers to select which model best mimics the source pose. We show that while poses with different self-contacts are considered different by observers in most cases, this effect is stronger for self-contacts involving the hands than for those involving the arms

Overview of the proposed approach. The encoder (green) generates an identity code for the target. We feed this code to the decoder (red) along with the source, which is concatenated with the decoder features at all resolution stages. The decoder finally outputs per vertex offsets from the input source towards the identity transfer result.

7.3.1 The Stare-in-the-Crowd Effect in Virtual Reality

Participants: Marc Christie, Ludovic Hoyet, Alberto Jovane, Anne-Hélène Olivier [contact], Pierre Raimbaud, Katja Zibrek.

Nonverbal cues are paramount in real-world interactions. Among these cues, gaze has received much attention in the literature. In particular, previous work has shown a search asymmetry between directed and averted gaze towards the observer using photographic stimuli, with faster detection and longer fixation towards directed gaze by the observer. This is known as the stare-in-the-crowd effect. In this work 38, we investigate whether stare-in-the crowd effect is preserved in Virtual Reality (VR). To this end, we designed a within-subject experiment where 30 human users were immersed in a virtual environment in front of an audience of 11 virtual agents (Figure 11), following 4 different gaze behaviours . We analysed the user's gaze behaviour when observing the audience, computing fixations and dwell time. We also collected the users' social anxiety score using a post-experiment questionnaire to control for some potential influencing factors. Results show that the stare-in-the-crowd effect is preserved in VR, as demonstrated by the significant differences between gaze behaviours, similarly to what was found in previous studies using photographic stimuli. Additionally, we found a negative correlation between dwell time towards directed gazes and users’ social anxiety scores. Such results are encouraging for the development of expressive and reactive virtual humans, which can be animated to express natural interactive behaviour. This work was performed in collaboration with Julien Pettré from the Virtus team and Claudio Pacchierotti from the Rainbow team.

7.3.2 The One-Man-Crowd: Single User Generation of Crowd Motions Using Virtual Reality

Participants: Marc Christie, Ludovic Hoyet [contact], Tairan Yin.

Crowd motion data is fundamental for understanding and simulating realistic crowd behaviours. Such data is usually collected through controlled experiments to ensure that both desired individual interactions and collective behaviours can be observed. It is however scarce, due to ethical concerns and logistical difficulties involved in its gathering, and only covers a few typical crowd scenarios. In this work 35, we propose and evaluate a novel Virtual Reality based approach lifting the limitations of real-world experiments for the acquisition of crowd motion data. Our approach immerses a single user in virtual scenarios where he/she successively acts as each crowd member. By recording the past trajectories and body movements of the user, and displaying them on virtual characters, the user progressively builds the overall crowd behaviour by him/herself. We validate the feasibility of our approach by replicating three real experiments (Figure 12), and compare both the resulting emergent phenomena and the individual interactions to existing real datasets. Our results suggest that realistic collective behaviours can naturally emerge from virtual crowd data generated using our approach, even though the variety in behaviours is lower than in real situations. These results provide valuable insights to the building of virtual crowd experiences, and reveal key directions for further improvements. This work was performed in collaboration with Julien Pettré from the Virtus team and Marie-Paule Cani from LIX.

7.3.3 Do You Need Another Hand? Investigating Dual Body Representations During Anisomorphic 3D Manipulation

Participants: Ludovic Hoyet [contact].

Illustration of two types of dual body representation studied in this work . On the left image, a ghost representation enables remote manipulation while a realistic co-located representation provides feedback with respect to the real user's position. On the right image, a ghost representation provides feedback with respect to the user's real position while a realistic representation enables precise manipulation with the environment thanks to slowed motion.

In virtual reality, several manipulation techniques distort users’ motions, for example to reach remote objects or increase precision. These techniques can become problematic when used with avatars, as they create a mismatch between the real performed action and the corresponding displayed action, which can negatively impact the sense of embodiment. In this work 20, we propose to use a dual representation during anisomorphic interaction. A co-located representation serves as a spatial reference and reproduces the exact users’ motion, while an interactive representation is used for distorted interaction (Figure 13). We conducted two experiments, investigating the use of dual representations with amplified motion (with the Go-Go technique) and decreased motion (with the PRISM technique). Two visual appearances for the interactive representation and the co-located one were explored. This exploratory study investigating dual representations in this context showed that people globally preferred having a single representation, but opinions diverged for the Go-Go technique. Also, we could not find significant differences in terms of performance. While interacting seemed more important than showing exact movements for agency during out-of-reach manipulation, people felt more in control of the realistic arm during close manipulation. This work was performed in collaboration with Ferran Argelaguet and Anatole Lécuyer from the Hybrid team.

7.4.1 Coupling dense point cloud correspondence and template model fitting for 3D human pose and shape reconstruction from a single depth image

Participants: Adnane Boukhayma, Franck Multon [contact].

In this work 40, we address the problem of capturing both the shape and the pose of a character using a single depth sensor. Some previous works proposed to fit a parametric generic human template in the depth image, while others developed deep learning (DL) approaches to find the correspondence between depth pixels and vertices of the template. In this paper, we explore the possibility of combining these two approaches to benefit from their respective advantages. The hypothesis is that DL dense correspondence should provide more accurate information to template model fitting, compared to previous approaches which only use estimated joint position only. Thus, we stacked a state-of-the-art DL dense correspondence method (namely double U- Net) and parametric model fitting (namely Simplify-X). The experiments on the SURREAL [1], DFAUST datasets [2] and a subset of AMASS [3], show that this hybrid approach enables us to enhance pose and shape estimation compared to using DL or model fitting separately. This result opens new perspectives in pose and shape estimation in many applications where complex or invasive motion capture set-ups are impossible, such as sports, dance, ergonomic assessment, etc.

This work was part of the European project SCHEDAR 36, funded by ANR, and leaded by Cyprus University. This work was performed in collaboration with University of Reims Champagne Ardennes.

7.4.2 Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature Space

Participants: Adnane Boukhayma [contact], Amine Ouasfi.

We explore a new idea for learning based shape reconstruction from a point cloud, based on the recently popularized implicit neural shape representations. We cast the problem as a few-shot learning of implicit neural signed distance functions in feature space, that we approach using gradient based meta-learning. We use a convolutional encoder to build a feature space given the input point cloud. An implicit decoder learns to predict signed distance values given points represented in this feature space. Setting the input point cloud, i.e. samples from the target shape function's zero level set, as the support (i.e. context) in few-shot learning terms, we train the decoder such that it can adapt its weights to the underlying shape of this context with a few (5) tuning steps. We thus combine two types of implicit neural network conditioning mechanisms simultaneously for the first time, namely feature encoding and meta-learning. Our numerical and qualitative evaluation shows that in the context of implicit reconstruction from a sparse point cloud, our proposed strategy, i.e. meta-learning in feature space, outperforms existing alternatives, namely standard supervised learning in feature space, and meta-learning in euclidean space, while still providing fast inference.

Overview of our method. Our input is a sparse point cloud (Support 𝐒i\mathbf {S}_i) and our output is an implicit neural SDF ff. ff is a neural network comprised of a convolutional encoder (top in gray) and an MLP decoder (bottom in gray). The decoder predicts SDF values for 3D points (red/blue circles) through their spatially sampled features (squares in shades of red/blue) from the encoder's activation maps. Following a gradient-based few-shot learning algorithm (MAML), we learn a meta-decoder in encoder feature space, parameterized with θ\theta , that can quickly adapt to a new shape, i.e. new parameters ϕi\phi _i, given its support. This is achieved by iterating per-shape 5-step adaptation gradient descent (orange arrow) using the support loss ℒSi\mathcal {L}_{S_i}, and one-step meta gradient-descent (green arrow) by back-propagating the Query set (QiQ_i) loss ℒQi\mathcal {L}_{Q_i} evaluated with the specialized parameters ϕi\phi _i w.r.t. the meta-parameters θ\theta . At test time, 5 fine-tuning iterations are performed similarly starting from the converged meta-model to evaluate ff.

7.4.3 Neural Mesh-Based Graphics

Participants: Adnane Boukhayma [contact], Franck Multon, Shubhendu Jena.

We revisit NPBG (Neural Point based Graphics), the popular approach to novel view synthesis that introduced the ubiquitous point feature neural rendering paradigm. We are interested in particular in data-efficient learning with fast view synthesis. We achieve this through a view-dependent mesh-based denser point descriptor rasterization, in addition to a foreground/background scene rendering split, and an improved loss. By training solely on a single scene, we outperform NPBG, which has been trained on ScanNet and then scene finetuned. We also perform competitively with respect to the state-of-the-art method SVS, which has been trained on the full dataset (DTU and Tanks and Temples) and then scene finetuned, in spite of their deeper neural renderer.

7.4.4 Musculosekeletal analysis library developments

Participants: Claire Livet, Georges Dumont [contact], Charles Pontonnier [contact].

Our team develops since a few years a musculoskeletal analysis toolbox (CusToM, that serves at a basis for many different PhD works now. We continue to enhance the capabilities of this toolbox by integrating PhD works. This year, we worked specifically on the development of closed loop models and optimisation methods. An alternative method for solving constrained multibody kinematics optimisation using a penalty method on constraints and a Levenberg–Marquardt algorithm is proposed. It is compared to an optimisation resolution with hard kinematic constraints. These methods are applied to two pairs of experiments and models. The penalty method was at least 20 times faster than the optimisation resolution while keeping similar reconstruction errors and constraints violation. The potential of the method is shown to accurately solve the multibody kinematics optimisation problem in a reasonable amount of time. A computational gain lies in implementing this resolution with a compiled and optimised program code 24. The work of Claire Livet was defended as a PhD Thesis work 48.

7.4.5 pressure insoles assessment for external forces prediction

Participants: Pauline Morin, Georges Dumont [contact], Charles Pontonnier [contact].

7.5.1 Early Recognition of Untrimmed Handwritten Gestures with Spatio-Temporal 3D CNN

Participants: Richard Kulpa [contact], William Mocaer.

Early recognition of untrimmed handwritten gestures is the task of recognizing as soon as possible gestures drawn in a continuous stream, one after another. This challenge is similar to early human wholebody motion recognition. This is particularly challenging for multi-touch gestures because it is impossible to know when the gesture has started and finished. For mono-stroke gestures, in an application context where the finger is never removed from the device between gestures, the recognition is even more complex. In this work we present an extension of the Online Long-Term Convolutional 3D (OLT-C3D) network to address the task of early recognition of untrimmed gestures which have been addressed by very few works. To evaluate our approach, we created two synthetic datasets using freely available benchmarks, MTGSetB and ILGDB, simulating the streaming data in two different application scenarios 41. Furthermore, we propose a new evaluation metric for this specific task. Our approach achieves good performances on the two new datasets and will be a baseline for future works on this challenging task.

7.5.2 Multiple Players Tracking in Virtual Reality: Influence of Soccer Specific Trajectories and Relationship With Gaze Activity

Participants: Richard Kulpa [contact], Anthony Sorel, Annabelle Limballe, Benoit Bideau, Alexandre Vu.

7.5.3 Visual tracking assessment in a soccer-specific virtual environment: A web-based study

Participants: Richard Kulpa [contact], Anthony Sorel, Annabelle Limballe, Benoit Bideau, Alexandre Vu.

7.5.4 Using Blur for Perceptual Investigation and Training in Sport? A Clear Picture of the Evidence and Implications for Future Research

Participants: Richard Kulpa [contact], Annabelle Limballe.

7.5.5 Acceptance by athletes of a virtual reality head-mounted display intended to enhance sport performance

Participants: Richard Kulpa [contact].

7.5.6 Influence of shoe torsional stiffness on foot and ankle biomechanics during tennis forehand strokes

Participants: Anthony Sorel [contact], Richard Kulpa, Benoit Bideau, Pierre Touzard.

7.5.7 Automatic Swimming Activity Recognition and Lap Time Assessment Based on a Single IMU: A Deep Learning Approach

Participants: Nicolas Bideau [contact], Guillaume Nicolas, Benoit Bideau, Erwan Delhaye, Antoine Bouvet.

7.5.8 Diving board characterization for diving analysis

Participants: Georges Dumont [contact], Charles Pontonnier, Guillaume Nicolas, Nicolas Bideau, Louise Demestre, Pauline Morin.

7.5.9 Gymnastics landings Analysis

Participants: Diane Haering [contact], Charles Pontonnier, Rebecca Crolan.

7.6.1 Exoskeleton biomechanical impact assessment

Participants: Charles Pontonnier [contact], Divyaksh Subhash Chander.

7.6.2 Wheelchair locomotion ergonomics

Participants: Charles Pontonnier [contact], Georges Dumont, Claire Livet.

7.7.1 Collision avoidance strategies between two athlete walkers: Understanding impaired avoidance behaviours in athletes with a previous concussion

Participants: Anne-Hélène Olivier [contact], Armel Crétual.

Left: Illustration of the real world image of two athletes interacting within the experiment to understand the effect of concussion on visuo-motor strategies. Right: Relative contributions to solve the collision depending on the group of athletes interacting. When 2 concussed athletes are involved in the interaction, the variability is increased .

Individuals who have sustained a concussion often display associated balance control deficits and visuomotor impairments despite being cleared by a physician to return to sport. Such visuomotor impairments can be highlighted in collision avoidance tasks that involves a mutual adaptation between two walkers. However, studies have yet to challenge athletes with a previous concussion during an everyday collision avoidance task, following return to sport. In this project performed in the frame of the Inria Bear Associate team, we investigated whether athletes with a previous concussion display associated behavioural changes during a 90 degree collision avoidance task with an approaching pedestrian (Cf. Figure 17, Left). To this end, thirteen athletes (ATH; 9 females, 23$\pm$4years) and 13 athletes with a previous concussion (CONC; 9 females, 22$\pm$3 years, concussion $<$6 months) walked at a comfortable walking speed along a 12.6m pathway while avoiding another athlete on a 90 degree collision course. Each participant randomly interacted with individuals from the same group 20 times and interacted with individuals from the opposite group 21 times. Minimum predicted distance (mpd) was used to examine collision avoidance behaviours between ATH and CONC groups. The overall progression of mpd(t) did not differ between groups (p>.05). During the collision avoidance task, previously concussed athletes contributed less when passing second compared to their peers(p<.001). When two previously concussed athletes were on a collision course, there was a greater amount of variability resulting in inappropriate adaptive behaviours (Cf. Figure 17 Right). Although successful at avoiding a collision with an approaching athlete, previously concussed athletes exhibit behavioural changes manifesting in riskier behaviours. The current findings suggest that previously concussed athletes possess behavioural changes even after being cleared to returned to sport, which may increase their risk of a subsequent injury when playing.

Cifre Moovency - Critère basé effort pour l'analyse in-situ de l'activité physique au travail : Application au port de charge bimanuel

Participants: Franck Multon [contact], Georges Dumont, Charles Pontonnier, Hasnaa Ouadoudi Belabizoui.

This Cifre contract has started in January 2022 for three years and is funding the PhD thesis of Hasnaa Ouadoudi-Belabizoui. It consists in building robust AI-based methods able to emulate inverse dynamics results from noisy-incomplete data to study the physical constraints of the operators in industrial workplaces. Indeed, ergonomics of such workplaces need to be assessed at the effort level and no reliable method enables such an assessment in-situ from a motion monitoring. The thesis aims at developing neural networks able to reproduce the results of a model based inverse dynamics method and then at constructing a reliable and synthetic indicator of the forces undergone by the operator during the execution of a given task.

The Cifre contracts funds the PhD salary and 10K€ per year for the supervision and management of the PhD thesis.

Cifre InterDigitial - Adaptive Avatar Customization for Immersive Experiences

Participants: Franck Multon [contact], Ludovic Hoyet, Nicolas Olivier.

This Cifre contract has started in February 2019 for three years and is funding the PhD thesis of Nicolas Olivier. The aim of the project is to design stylized avatars of users in immersive environment and digital arts such as videogames or cinema.

To this end, we will design a pipeline from motion and shape capture of the user to the simulation of the 3D real-time and stylized avatar. It will take hairs, eyes, face, body shape and motion into account. The key idea is to stylized both appaearance and motion to make avatar better correspond to the style of the movie of immersive experience. We will carry-out perceptual studies to better understand the expectation of the users when controlling stylized avatars, to maximize embodiment. The Cifre contracts funds the PhD salary and 15K€ per year for the supervision and management of the PhD thesis. This contract is also in collaboration with Hybrid team. The PhD has been defended in March 2022.

Cifre InterDigitial - Learning-Based Human Character Animation Synthesis for Content Production

Participants: Ludovic Hoyet [contact], Lucas Mourot.

The overall objective of the PhD thesis of Lucas Mourot, which started in June 2020, is to adapt and improve the state-of-art on video animation and human motion modelling to develop a semi-automated framework for human animation synthesis that brings real benefits to artists in the movie and advertising industry. In particular, one objective is to leverage the use of novel learning-based approaches, in order to propose skeleton-based animation representations, as well as editing tools, in order to improve the resolution and accuracy of the produced animations, so that automatically synthetized animations might become usable in an interactive way by animation artists.

Collaboration with company SolidAnim (Bordeaux, France)

Participants: Marc Christie [contact], Xi Wang.

This contract started in November 2019 for three years. Its purpose is to explore novel means of performing depth detection for augmented reality applied to the film and broadcast industries. The grant serves to fund the PhD of Xi Wang. (160kE)

Collaboration Unreal company (Unreal Megagrant)

Participants: Marc Christie [contact].

This contract started in September 2020 for two years. The objective is to explore means of designing novel VR manipulators for character animation tasks in Unreal Engine. (70kE)

9.1.1 Inria associate team not involved in an IIL or an international program

9.2.1 Visits of international scientists

9.3.1 Horizon Europe

9.3.2 H2020 projects

9.3.3 ANR

9.3.4 Défi Avatar

Participants: Jean Basset, Ludovic Hoyet [contact], Maé Mavromatis, Franck Multon.

This project aims at design avatars (i.e., the user’s representation in virtual environments) that are better embodied, more interactive and more social, through improving all the pipeline related to avatars, from acquisition and simulation, to designing novel interaction paradigms and multi-sensory feedback. It involves 6 Inria teams (GraphDeco, Hybrid, Loki, MimeTIC, Morpheo, Potioc), Prof. Mel Slater (Uni. Barcelona), and 2 industrial partners (InterDigitak and Faurecia).

Website

9.3.5 Défi Ys.AI

Participants: Franck Multon [contact], Ludovic Hoyet, Adnane Boukhayma, Tangui Marchand Guerniou.

The metaverse became a buzzword following recent announcements about massive investments on those new universes. While it is not a new concept, second life and a few other past attempts opened the door to this new way to communicate. It can be seen as the future of the social and professional immersive communication for the emergent AI-based e-society, and it is based on a large number of technologies (AR/VR/XR, blockchain/NFT, AI, CG, low latency networks, interactions, coding, rendering...).

Interdigital foresees high potential in the development of metaverse technologies and associated representation formats. However, the panel of technologies to be addressed is too large and some selection has to be done. One of those, is the representation of the humans within those mixed real and virtual environments and how they interact between each other and with the scene (inc. the objects). The project goal is thus to address this topic with a special focus on the representation formats for digital avatars and their behavior in a digital and responsive environment. It includes the study of deep learning and, more generally, artificial intelligence technologies applied to interactions and animation of avatars and associated representation format(s). The main challenge is to solve the uncanny valley effect to provide users with a natural and lifelike social interaction between real and virtual actors, leading to full engagement in those future metaverse experiences.

9.3.6 ADT PyToM

Participants: Charles Pontonnier [contact], Laurent Guillo, Georges Dumont, Salomé Ribault.

9.4.1 KIMEA Cloud Project

Participants: Franck Multon [contact], Adnane Boukhayma, Shubhendu Jena.

The Region Bretagne with “Image & Réseaux” national clusters have funded the project “Kimea Cloud” (Sept 2020-June 2022). This project leaded by Moovency (start-up company created in 2018 based on a PhD from MimeTIC) aims at proposing a new system to assess painful movement at work using ligth devices, such as a simple camera. Quortex (www.quortex.io/) company provides its expertise in delivering video content on Internet to make the assessment be remote and efficient for the future customers. MimeTIC provides a robust 3D posture tracking system based on the video stream.

9.4.2 EXOSCARNE Project

Participants: Charles Pontonnier [contact], Simon Kirchhofer.

9.4.3 Stratégie d'Attractivité Durable (SAD) REACTIVE

Participants: Anne-Hélène Olivier [contact], Ludovic Hoyet, Pierre Raimbaud, Katja Zibrek.

This project (2020-2022) is funded by the Brittany region. The general context of REACTIVE concerns the interactions between users and reactive and expressive virtual humans. A prerequisite for the creation of such virtual humans is to understand how "real" humans interact and react to the behaviors and reactions of others. This is the core of the REACTIVE project. It focuses on the elements of non-verbal communication derived from body movements. The underlying idea is to identify, through an analysis involving the Movement Sciences, invariants within the body in reactions to situations of interaction between several people. Through an approach based on Digital Sciences, we propose to model these invariants to create new animations of reactive and expressive virtual humans. This project was performed in collaboration with Julien Pettré and Claudio Pacchierotti.

10.1.1 Scientific events: organisation

10.1.2 Scientific events: selection

10.1.3 Journal

10.1.4 Invited talks

• Charles Pontonnier: Sciences 2024 seminars, GDR CNRS robotique, SCI-Rennes

10.1.5 Leadership within the scientific community

• Ludovic Hoyet: International Journal of Human - Computer Studies

10.1.6 Scientific expertise

• Anne-Hélène Olivier: Expertise for ESR Program,Région Nouvelle Aquitaine and ANR AAPG,Interfaces:digital sciences-humanities and social sciences
• Charles Pontonnier: ANR AAPG,Swiss National Science Foundation

10.1.7 Research administration

• Ludovic Hoyet is the coordinator of the Inria Research Challenge Avatar
• Ludovic Hoyet is the coordinator of the ANR JCJC Per${}^2$
• Franck Multon is responsible for the coordination of national Inria actions in Sports
• Franck Multon is the scientific representative of Inria in Sciences2024 group and scientific Committee
• Franck Multon is the scientific representative of Inria in the EUR Digisport steering committe and scientific committee
• Franck Multon is the co-director of the "Nemo.AI" joint Lab with InterDigital, and the associated Défi Ys.AI
• Franck Multon is member of the Brittany commission of deontology
• Armel Crétual is the elected head of the Sports Sciences departement (STAPS) in University Rennes 2
• Benoit Bideau is the head of the M2S Laboratory
• Benoit Bideau is vice-president of University Rennes 2, in charge of the valorisation
• Benoit Bideau is the leader of the EUR DIGISPORT project
• Charles Pontonnier is the deputy director of the Mechatronics teaching and research department of ENS Rennes
• Charles Pontonnier is member of the EUR digisport pedagogical comitee
• Anne-Hélène Olivier is a co-director of the master program in adapted physical activity at the University of Rennes 2
• Richard Kulpa is the co-leader of the EUR DIGISPORT project
• Richard Kulpa is the scientific head of the EUR DIGISPORT project
• Richard Kulpa is the leader of the PPR REVEA project
• Marc Christie is the Project Coordinator of the H2020 ICT-55 project INVICTUS

10.2.1 Supervision

• PhD stopped (beginning January 2019, ended April 2022): Nils Hareng, simulation of plausible bipedal locomotion of human and non-human primate, University Rennes 2, Franck Multon, & Bruno Watier (CNRS LAAS in Toulouse)
• PhD defended (beginning January 2019, defended March 2022): Nicolas Olivier, Adaptive Avatar Customization for Immersive Experience, Cifre InterDigital, Franck Multon, Ferran Arguelaget (Hybrid team), Quentin Avril (InterDigital), Fabien Danieau (InterDigital)
• PhD in progress (beginning September 2017): Lyse Leclerc, Intérêts dans les activités physiques du rétablissement de la fonction inertielle des membres supérieurs en cas d’amputation ou d’atrophie, Armel Crétual, Diane Haering
• PhD defended (beginning September 2018, defended June 2022) : Jean Basset, Learning Morphologically Plausible Pose Transfer, Inria, Edmond Boyer (Morpheo Inria Grenoble), Franck Multon
• PhD in progress (beginning December 2020): Mohamed Younes, Learning and simulating strategies in sports for VR training, University Rennes 1, Franck Multon, Richard Kulpa, Ewa Kijak, Simon Malinowski
• PhD defended (beginning september 2019, defended july 2022): Claire Livet, Contributions algorithmiques à l’analyse musculo-squelettique : modèles et méthodes, Ecole normale supérieure, Georges Dumont, Charles Pontonnier
• PhD defended (beginning october 2019, defended november 2022): Louise Demestre, Analyse de critères mécaniques de la performance en plongeon sur tremplin à l’aide d’un modèle d’interaction plongeur plongeoir, Ecole normale supérieure, Georges Dumont, Charles Pontonnier, Nicolas Bideau, Guillaume Nicolas
• PhD defended (beginning October 2018, defended May 2022): Benjamin Niay, A framework for synthezing personalised human motions from motion capture data and perceptual information, Ludovic Hoyet, Anne-Hélène Olivier, Julien Pettré (Virtus team).
• PhD in progress (beginning September 2019): Alberto Jovane, Modélisation de mouvements réactifs et comportements non verbaux pour la création d’acteurs digitaux pour la réalité virtuelle, Marc Christie, Ludovic Hoyet, Claudio Pacchierotti (Rainbow team), Julien Pettré (Virtus team).
• PhD defended (beginning November 2019, defended November 2022): Adèle Colas, Modélisation de comportements collectifs réactifs et expressifs pour la réalité virtuelle, Ludovic Hoyet, Anne-Hélène Olivier, Claudio Pacchierotti (Rainbow team), Julien Pettré (Virtus team).
• PhD in progress (beginning September 2018): Carole Puil, Impact d’une stimulation plantaire orthétique sur la posture d’individus sains et posturalement déficients au cours de la station debout, et lors de la marche, Armel Crétual, Anne-Hélène Olivier
• PhD in progress (beginning september 2019): Annabelle Limballe, Anticipation dans les sports de combat : la réalité virtuelle comme solution innovante d’entraînement, Richard Kulpa & Simon Bennett & Benoit Bideau
• PhD in progress (beginning September 2019): Alexandre Vu, Evaluation de l’influence des feedbacks sur la capacité d’apprentissage dans le cadre d’interactions complexes entre joueurs et influence de ces feedbacks en fonction de l’activité sportive, Richard Kulpa & Benoit Bideau & Anthony Sorel
• PhD in progress (beginning September 2019): William Mocaer, Réseaux de Neurones à Convolution Spatio-Temporelle pour l'analyse et la reconnaissance précoce d’actions et de gestes, Eric Anquetil & Richard Kulpa
• PhD in progress (beginning September 2020): Pauline Morin, Adaptation des méthodes prédiction des efforts d’interaction pour l’analyse biomécanique du mouvement en milieu écologique, Ecole normale supérieure de Rennes, direction Georges Dumont et Charles Pontonnier
• PhD in progress (beginning June 2020): Lucas Mourot, Learning-Based Human Character Animation Synthesis for Content Production, Pierre Hellier (InterDigital), Ludovic Hoyet, François Le Clerc (InterDigital).
• PhD in progress (beginning September 2020): Agathe Bilhaut, Stratégies perceptivo-motrices durant la locomotion des patients douloureux chroniques : nouvelles méthodes d’analyse et de suivi, Armel Crétual, Anne-Hélène Olivier, Mathieu Ménard (Institut Ostéopathie Rennes, M2S)
• PhD in progress (beginning October 2020): Emilie Leblong, Prise en compte des interactions sociales dans un simulateur de conduite de fauteuil roulant électrique en réalité virtuelle : favoriser l’apprentissage pour une mobilité inclusive, Anne-Hélène Olivier, Marie Babel (Rainbow team)
• PhD in progress (beginning November 2020): Thomas Chatagnon, Micro-to-macro energy-based interaction models for dense crowds behavioral simulations, Ecole normale supérieure de Rennes, Ludovic Hoyet, Anne-Hélène Olivier,Charles Pontonnier, Julien Pettré (Virtus team).
• PhD in progress (beginning November 2020): Vicenzo Abichequer-Sangalli, Humains virtuels expressifs et réactifs pour la réalité virtuelle, Marc Christie, Ludovic Hoyet, Carol O'Sullivan (TCD, Ireland), Julien Pettré (virtus team).
• PhD in progress (beginning November 2020): Tairan Yin, Création de scènes peuplées dynamiques pour la réalité virtuelle, Marc Christie, Marie-Paule Cani (LIX), Ludovic Hoyet, Julien Pettré (Virtus team).
• PhD in progress (beginning October 2020): Qian Li, Neural novel view synthesis of dynamic people from monocular videos, Adnane Boukhayma, Franck Multon.
• PhD in progress (beginning June 2022): Shubhendu Jena, Combining implicit and explicit representations for modeling 3D Shape and appearance, Adnane Boukhayma, Franck Multon.
• PhD in progress (beginning November 2022): Sony Saint-Auret, Virtual Collaborative « Jeu de Paume », Ronan Gaugne, Valérie Gouranton, Franck Multon, Richard Kulpa.
• PhD in progress (beginning October 2021): Maé Mavromatis, Towards “Avatar-Friendly” Characterization of Virtual Reality Interaction Methods, Ferran Argelaguet (Hybrid team), Ludovic Hoyet, Anatole Lécuyer (Hybrid team).
• PhD in progress (beginning October 2021): Rebecca Crolan, Prediction of low back load during gymnastics landings for the prevention and follow-up of athlete injuries, Charles Pontonnier, Diane Haering, Matthieu Ménard (M2S Lab).
• PhD in progress (beginning November 2021): Rim Rekik, Learning and evaluating 3D human motion synthesis, Anne-Hélène Olivier, Stefanie Wuhrer (Morpheo team).
• PhD in progress (beginning Jan. 2022): Yulia Patotskaya, Appealing Character Design for Embodied Virtual Reality, Ludovic Hoyet, Julien Pettré (Virtus team), Katja Zibrek.
• PhD in progress (beginning Jan. 2022): Hasnaa Ouadoudi Belabzioui, Force-based criterion for in-situ analysis of physical activity at work: application to load carrying, Charles Pontonnier Franck Multon, Georges Dumont.
• PhD in progress (beginning Oct. 2022): Alexis Jensen, Dense Crowd Simulation by physics modeling, Julien Pettré (Virtus Team), Charles Pontonnier.
• PhD in progress (beginning Oct. 2022): Etienne Ricard, Musculoskeletal modeling of the human-exoskeleton system: evaluation and monitoring of exoskeleton familiarization indicators, Charles Pontonnier, Chris Hayot (INRS), Kevin Desbrosses (INRS).

10.2.2 Juries

• PhD defense: Univ. Rennes 1, Benjamin Niay, "Evaluation and use of biomechanical parameters for virtual humans walking animations" may 2022, Franck Multon, president
• HDR defense: INSA Rennes, Valérie Gouranton, "Modèles et Outils pour la Production d’Applications de RX, Focus sur le Patrimoine Culturel", March 2022, Franck Multon, President
• PhD defense: Université de Grenoble-Alpes de Victor Romero, « Un environnement virtuel immersif, interactif et collaboratif pour les revues de conception basées sur les modèles », 13 décembre 2022, Georges Dumont, Reviewer
• PhD defense: Université Lyon 1 Claude Bernard de Thomas Bonis. « Simulation directe prédictive de marches pathologiques ». 13 octobre 2022, Georges Dumont, Reviewer
• PhD defense: Thèse de doctorat de Aix-Marseille Université de Mathieu Caumes. «Rôle de la relation de force longueur sur la coordination musculaire et la production de force avec la main : Approche par modélisation biomécanique ». 28 avril 2022, Georges Dumont, Reviewer
• PhD defense: Université Lyon 1 Claude Bernard de Vincent Gibeaux. « Motion simulator for accessibility ». december 2022, Charles Pontonnier, Reviewer
• PhD defense: Ecole Centrale de Nantes de Vamsi Krishna Gruda. « Contributions to utilize a Cobot as intermittent contact haptic interfaces in virtual reality». august 2022, Charles Pontonnier, Reviewer

International journals

• 11 articleF.Florian Berton, F.Fabien Grzeskowiak, A.Alexandre Bonneau, A.Alberto Jovane, M.Marco Aggravi, L.Ludovic Hoyet, A.-H.Anne-Hélène Olivier, C.Claudio Pacchierotti and J.Julien Pettré. Crowd Navigation in VR: exploring haptic rendering of collisions.IEEE Transactions on Visualization and Computer Graphics287July 2022, 2589 - 2601
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• 12 articleB.Beatriz Cabrero-Daniel, R.Ricardo Marques, L.Ludovic Hoyet, J.Julien Pettré and J.Joseph Blat. Dynamic Combination of Crowd Steering Policies Based on Context.Computer Graphics Forum412May 2022, 209-219
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• 13 articleD. S.Divyaksh Subhash Chander, S.Stéphane Marie, M.Mathieu Gréau and C.Charles Pontonnier. Biomechanical requirements of meat cutting tasks: an overview.Computer Methods in Biomechanics and Biomedical Engineering2022, 1-2
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• 14 articleA.Adèle Colas, W.Wouter van Toll, K.Katja Zibrek, L.Ludovic Hoyet, A.-H.Anne-Hélène Olivier and J.Julien Pettré. Interaction Fields: Intuitive Sketch-based Steering Behaviors for Crowd Simulation.Computer Graphics ForumApril 2022, 1-14
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• 15 articleR.Rebecca Crolan, D.Diane Haering, S.Suzon Pucheu, M.Mathieu Ménard and C.Charles Pontonnier. Characterization of the energy dissipation of a landing mat for the study of landing strategies in artistic gymnastics.Computer Methods in Biomechanics and Biomedical Engineering2022, 1-2
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• 16 articleE.Erwan Delhaye, A.Antoine Bouvet, G.Guillaume Nicolas, J. P.João Paulo Vilas-Boas, B.Benoit Bideau and N.Nicolas Bideau. Automatic Swimming Activity Recognition and Lap Time Assessment Based on a Single IMU: A Deep Learning Approach.Sensors22152022, 5786
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• 17 articleL.Louise Demestre, N.Nicolas Bideau, G.Guillaume Nicolas, C.Charles Pontonnier and G.Georges Dumont. Models and methods for analyzing interaction in springboard diving.Computer Methods in Biomechanics and Biomedical Engineering2022, 1-2
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• 18 articleL.Louise Demestre, S.Stéphane Grange, C.Cécile Dubois, N.Nicolas Bideau, G.Guillaume Nicolas, C.Charles Pontonnier and G.Georges Dumont. Characterization of the dynamic behavior of a diving board using motion capture data.Sports Engineering2022, 1-18
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• 19 articleL.Louise Demestre, P.Pauline Morin, F.François May, N.Nicolas Bideau, G.Guillaume Nicolas, C.Charles Pontonnier and G.Georges Dumont. Motion-based ground reaction forces and moments prediction method for interaction with a moving and/or non-horizontal structure.Journal of Biomechanical Engineering2022, 1-9
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• 20 articleD.Diane Dewez, L.Ludovic Hoyet, A.Anatole Lécuyer and F.Ferran Argelaguet. Do You Need Another Hand? Investigating Dual Body Representations During Anisomorphic 3D Manipulation.IEEE Transactions on Visualization and Computer Graphics2022, 1-11
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• 21 articleA.Anïs Farr, R.R Le Crolan, M.Mathieu Menard, C.Charles Pontonnier and D.Diane Haering. Prediction of lumbar fatigue during gymnastics landings using statistical modelling and Machine Learning.Computer Methods in Biomechanics and Biomedical Engineering2022
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• 22 articleA.Abdallah Fayssoil, L. S.Lee S Nguyen, T.Tanya Stojkovic, H.Helene Prigent, R.Robert Carlier, H.Helge Amthor, J.Jean Bergounioux, J.Justine Zini, S.Sebastien Damez‐fontaine, K.Karim Wahbi, P.Pascal Laforet, G.Guillaume Nicolas, A.Anthony Behin, G.Guillaume Bassez, F.France Leturcq, R.Rabah Ben Yaou, N.Nicolas Mansencal, D.Djillali Annane, F.Frédéric Lofaso and D.David Orlikowski. Determinants of diaphragm inspiratory motion, diaphragm thickening, and its performance for predicting respiratory restrictive pattern in Duchenne muscular dystrophy.Muscle & nerve. Supplement.651January 2022, 89-95
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• 23 articleA.Annabelle Limballe, R.Richard Kulpa and S.Simon Bennett. Using Blur for Perceptual Investigation and Training in Sport? A Clear Picture of the Evidence and Implications for Future Research.Frontiers in Psychology12March 2022, 1-24
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• 24 articleC.Claire Livet, T.Théo Rouvier, C.Christophe Sauret, H.Hélène Pillet, G.Georges Dumont and C.Charles Pontonnier. A penalty method for constrained Multibody kinematics optimisation using a Levenberg-Marquardt algorithm.Computer Methods in Biomechanics and Biomedical Engineering2022, 1-27
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• 25 articleC.Caroline Martin, P.Pierre Touzard, N.Nicolas Horvais, P.Pierre Puchaud, R.Richard Kulpa, B.Benoit Bideau and A.Anthony Sorel. Influence of shoe torsional stiffness on foot and ankle biomechanics during tennis forehand strokes.European Journal of Sport Science2022
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• 26 articleN.Nicolas Mascret, G.Gilles Montagne, A.Antoine Devrièse-Sence, A.Alexandre Vu and R.Richard Kulpa. Acceptance by athletes of a virtual reality head-mounted display intended to enhance sport performance.Psychology of Sport and ExerciseApril 2022, 102201
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• 27 articleP.Pauline Morin, A.Antoine Muller, C.Charles Pontonnier and G.Georges Dumont. Evaluation of the Foot Center of Pressure Estimation from Pressure Insoles during Sidestep Cuts, Runs and Walks.Sensors2215July 2022, 11
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• 28 articleN.Nicolas Olivier, G.Glenn Kerbiriou, F.Ferran Argelaguet Sanz, Q.Quentin Avril, F.Fabien Danieau, P.Philippe Guillotel, L.Ludovic Hoyet and F.Franck Multon. Study on Automatic 3D Facial Caricaturization: From Rules to Deep Learning.Frontiers in Virtual Reality2January 2022, 1-15
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• 29 articleT.Théo Rouvier, C.Claire Livet, A.Antoine Lombart, G.Georges Dumont, C.Charles Pontonnier, C.Christophe Sauret and H.Hélène Pillet. Comparison of scapula soft tissue artefact compensation methods during manual wheelchair locomotion.Gait & Posture97September 2022, S233 - S234
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• 30 articleT.Théo Rouvier, A.Aude Louessard, E.Emeline Simonetti, S.Samuel Hybois, J.Joseph Bascou, C.Charles Pontonnier, H.Hélène Pillet and C.Christophe Sauret. Manual wheelchair biomechanics while overcoming various environmental barriers: A systematic review.PLoS ONE176June 2022, 1-21
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• 31 articleN.Natalie Snyder, M.Michael Cinelli, V.Victoria Rapos, A.Armel Crétual and A.-H.Anne-Hélène Olivier. Collision avoidance strategies between two athlete walkers: Understanding impaired avoidance behaviours in athletes with a previous concussion.Gait & Posture922022, 24-29
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• 32 articleA.Alexandre Vu, A.Anthony Sorel, C.Charles Faure, A.Antoine Aurousseau, B.Benoit Bideau and R.Richard Kulpa. Visual tracking assessment in a soccer-specific virtual environment: A web-based study.PLoS ONE1762022, 0269643
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• 33 articleA.Alexandre Vu, A.Anthony Sorel, A.Annabelle Limballe, B.Benoit Bideau and R.Richard Kulpa. Multiple Players Tracking in Virtual Reality: Influence of Soccer Specific Trajectories and Relationship With Gaze Activity.Frontiers in Psychology13May 2022, 1-14
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• 34 articleX.Xi Wang, M.Marc Christie and E.Eric Marchand. Binary Graph Descriptor for Robust Relocalization on Heterogeneous Data.IEEE Robotics and Automation Letters722022, 2008 - 2015
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• 35 articleT.Tairan Yin, L.Ludovic Hoyet, M.Marc Christie, M.-P.Marie-Paule Cani and J.Julien Pettré. The One-Man-Crowd: Single User Generation of Crowd Motions Using Virtual Reality.IEEE Transactions on Visualization and Computer Graphics2022, 1-11
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International peer-reviewed conferences

• 36 inproceedingsA.Andreas Aristidou, A.Alan Chalmers, Y.Yiorgos Chrysanthou, C.C Loscos, F.F Multon, J.J Parkins, B.Bhuvan Sarupuri and E.Efstathios Stavrakis. Safeguarding our Dance Cultural Heritage.Eurographics 2022 - 43nd Annual Conference of the European Association for Computer GraphicsEurographics 2022 - TutorialsReims, FranceApril 2022, 1-6
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• 37 inproceedingsJ.Jean Basset, B.Badr Ouannas, L.Ludovic Hoyet, F.Franck Multon and S.Stefanie Wuhrer. Impact of Self-Contacts on Perceived Pose Equivalences.MIG 2022 - ACM SIGGRAPH Conference on Motion, Interaction and GamesGuanajuato Mexico, MexicoACMNovember 2022, 1-10
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• 38 inproceedingsP.Pierre Raimbaud, A.Alberto Jovane, K.Katja Zibrek, C.Claudio Pacchierotti, M.Marc Christie, L.Ludovic Hoyet, J.Julien Pettre and A.-H.Anne-Hélène Olivier. The Stare-in-the-Crowd Effect in Virtual Reality.VR 2022 - IEEE Conference on Virtual Reality and 3D User InterfacesChristchurch, New ZealandIEEEMarch 2022, 281-290
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• 39 inproceedingsJ.João Regateiro and E.Edmond Boyer. Temporal Shape Transfer Network for 3D Human Motion.3DV 2022 - International Conference on 3D VisionPrague / Hybrid, Czech RepublicSeptember 2022, 1-9
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• 40 inproceedingsX.Xiaofang Wang, A.Adnane Boukhayma, S.Stéphanie Prévost, E.Eric Desjardin, C.Céline Loscos and F.Franck Multon. Coupling dense point cloud correspondence and template model fitting for 3D human pose and shape reconstruction from a single depth image.International Conference on Interactive Media, Smart Systems and Emerging Technologies (IMET)Limassol, Cyprus2022, 1-8
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Conferences without proceedings

• 41 inproceedingsW.William Mocaër, E.Eric Anquetil and R.Richard Kulpa. Early Recognition of Untrimmed Handwritten Gestures with Spatio-Temporal 3D CNN.ICPR 2022 - International Conference on Pattern RecognitionMontréal, CanadaAugust 2022, 1636-1642
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• 42 inproceedingsW.William Mocaër, E.Eric Anquetil and R.Richard Kulpa. Réseau Convolutif Spatio-Temporel 3D pour la Reconnaissance Précoce de Gestes Manuscrits Non-Segmentés.RFIAP 2022 - Congrès Reconnaissance des Formes, Image, Apprentissage et PerceptionVannes, FranceJuly 2022, 1-9
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• 43 inproceedingsA.Amine Ouasfi and A.Adnane Boukhayma. Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature Space.ECCV 2022 - European Conference on Computer VisionLNCS-13692Computer Vision – ECCV 2022Tel Aviv, IsraelSpringer Nature SwitzerlandJuly 2022, 561-578
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Scientific book chapters

• 44 inbookS.Stéphane Salvan, F.Florence Maqueda and C.Charles Pontonnier. Un exemple d'archéologie du geste martial: les guerriers de Paestum.Armes et GuerriersBAR international seriesS3078BAR PublishingApril 2022, 1-12
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Doctoral dissertations and habilitation theses

• 45 thesisJ.Jean Basset. Morphologically Plausible Deformation Transfer.Université Grenoble Alpes [2020-....]June 2022
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• 46 thesisL.Louise Demestre. Analysis of mechanical criteria of springboard diving performance using a diver-diving board interaction model.École normale supérieure de RennesNovember 2022
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• 47 thesisL.Ludovic Hoyet. Towards Perception-based Character Animation.Université Rennes 1May 2022
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• 48 thesisC.Claire Livet. Algorithmic contributions to musculoskeletal analysis ˸ models and methods.École normale supérieure de RennesJuly 2022
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• 49 thesisN.Nicolas Olivier. Adaptive avatar customization for immersive experiences.Université Rennes 1March 2022
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Reports & preprints

• 50 miscS.Shubhendu Jena, F.Franck Multon and A.Adnane Boukhayma. Neural Mesh-Based Graphics.January 2023
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• 51 miscM.Mathieu Marsot, S.Stefanie Wuhrer, J.-S.Jean-Sebastien Franco and A. H.Anne Hélène Olivier. Representing motion as a sequence of latent primitives, a flexible approach for human motion modelling.September 2022
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Other scientific publications

• 52 inproceedingsA.Agathe Bilhaut, A.Armel Crétual, M.Mathieu Ménard and A.-H.Anne-Hélène Olivier. Locomotion behavior of chronic non-specific low back pain (cNSLBP) patient while walking through apertures.ISPGR 2022 - Congres on International Society of Posture & Gait ResearchMontréal, CanadaJuly 2022
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• 53 inproceedingsA.Agathe Bilhaut, M.Mathieu Ménard, O.Olivier Roze, A.Armel Crétual and A.-H.Anne-Hélène Olivier. Etude du paradigme de passage à travers une ouverture horizontale dans une population présentant une lombalgie chronique commune.SOFPELMarseille, FranceDecember 2022
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• 54 inproceedingsA.-H.Anne-Hélène Olivier, M.Marie Babel, A.Armel Crétual, L.Leborgne Nicolas, L.Louise Devigne, F.François Pasteau, B.Bastien Fraudet, E.Emilie Leblong and J.Julien Pettré. Comment rejoindre un groupe quand on se déplace en fauteuil ? Etude observationnelle biomécanique des interactions piétons-usagers de fauteuil roulant.SOFMER 2022 - 37e congrès de la Société Française de Médecine Physique et de RéadaptationRennes, FranceDecember 2022
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• 55 inproceedingsA.-H.Anne-Hélène Olivier, A.Antoine Marin, J.Joris Boulo, A.Aurélie Dommes, G.Gaetan Merlhiot, N.-T.Nguyen-Thong Dang, F.Fabrice Vienne, R.Régis Lobjois, V.Viola Cavallo and A.Armel Crétual. Body movement strategies to initiate the crossing of a street in front of traditional and self-driving cars in young and older adults.ISPGR 2022 - Congres on International Society of Posture & Gait ResearchMontréal, CanadaJuly 2022, 1-1
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• 56 inproceedingsC.Carole Puil, A.Armel Crétual and A.-H.Anne-Hélène Olivier. Postural static task on a thin foam pad: evaluation of the plantar cutaneous system.ISPGR 2022 - Congres on International Society of Posture & Gait ResearchMontréal, CanadaJuly 2022
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• 57 inproceedingsX.X Wang, A.Adnane Boukhayma, S.Stéphanie Prevost, E.Eric Desjardin, C.C Loscos and F.Franck Multon. 3D Human Shape and Pose from a Single Depth Image with Deep Dense Correspondence Enabled Model Fitting.Eurographics 2022 - 43nd Annual Conference of the European Association for Computer GraphicsReims, FranceApril 2022, 1-2
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• 58 inproceedingsM.Mohamed Younes, E.Ewa Kijak, R.Richard Kulpa, S.Simon Malinowski and F.Franck Multon. AIP: Adversarial Interaction Priors for Multi-Agent Physics-based Character Control.SIGGRAPH Asia 2022 - 15th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in AsiaSIGGRAPH Asia 2022 PostersDaegu, South KoreaDecember 2022, 2
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• 59 inproceedingsK.Katja Zibrek, B.Benjamin Niay, A.-H.Anne-Hélène Olivier, L.Ludovic Hoyet, J.Julien Pettré and R.Rachel Mcdonnell. Proximity in VR: The Importance of Character Attractiveness and Participant Gender.VR 2022 - IEEE Conference on Virtual Reality and 3D User InterfacesChristchurch, New ZealandMarch 2022, 1-2
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