3.1 Shape and Appearance Modeling

Standard acquisition platforms, including commercial solutions proposed by companies such as Microsoft, 3dMD or 4DViews, now give access to precise 3D models with geometry, e.g. meshes, and appearance information, e.g. textures. Still, state-of-the-art solutions are limited in many respects: They generally consider limited contexts and close setups with typically at most a few meter side lengths. As a result, many dynamic scenes, even a body running sequence, are still challenging situations; They also seldom exploit time redundancy; Additionally, data driven strategies are yet to be fully investigated in the field. The MORPHEO team builds on the Kinovis platform for data acquisition and has addressed these issues with, in particular, contributions on time integration, in order to increase the resolution for both shapes and appearances, on representations, as well as on exploiting machine learning tools when modeling dynamic scenes. Our originality lies, for a large part, in the larger scale of the dynamic scenes we consider as well as in the time super resolution strategy we investigate. Another particularity of our research is a strong experimental foundation with the multiple camera Kinovis platforms.

3.2 Dynamic Shape Vision

Dynamic Shape Vision refers to research themes that consider the motion of dynamic shapes, with e.g. shapes in different poses, or the deformation between different shapes, with e.g. different human bodies. This includes for instance shape tracking and shape registration, which are themes covered by MORPHEO. While progress has been made over the last decade in this domain, challenges remain, in particular due to the required essential task of shape correspondence that is still difficult to perform robustly. Strategies in this domain can be roughly classified into two categories: (i) data driven approaches that learn shape spaces and estimate shapes and their variations through space parameterizations; (ii) model based approaches that use more or less constrained prior models on shape evolutions, e.g. locally rigid structures, to recover correspondences. The MORPHEO team is substantially involved in both categories. The second one leaves more flexibility for shapes that can be modeled, an important feature with the Kinovis platform, while the first one is interesting for modeling classes of shapes that are more likely to evolve in spaces with reasonable dimensions, such as faces and bodies under clothing. The originality of MORPHEO in this axis is to go beyond static shape poses and to consider also the dynamics of shape over several frames when modeling moving shapes, and in particular with shape tracking and animation.

3.3 Inside Shape Vision

Another research axis is concerned with the ability to perceive inside moving shapes. This is a more recent research theme in the MORPHEO team that has gained importance. It was originally the research associated to the Kinovis platform installed in the Grenoble Hospitals. This platform is equipped with two X-ray cameras and ten color cameras, enabling therefore simultaneous vision of inside and outside shapes. We believe this opens a new domain of investigation at the interface between computer vision and medical imaging. Interesting issues in this domain include the links between the outside surface of a shape and its inner parts, especially with the human body. These links are likely to help understanding and modeling human motions. Until now, numerous dynamic shape models, especially in the computer graphics domain, consist of a surface, typically a mesh, rigged to a skeletal structure that is never observed in practice but allows to parameterize human motion. Learning more accurate relationships using observations can therefore significantly impact the domain.

3.4 Shape Animation

3D animation is a crucial part of digital media production with numerous applications, in particular in the game and motion picture industry. Recent evolutions in computer animation consider real videos for both the creation and the animation of characters. The advantage of this strategy is twofold: it reduces the creation cost and increases realism by considering only real data. Furthermore, it allows to create new motions, for real characters, by recombining recorded elementary movements. In addition to enable new media contents to be produced, it also allows to automatically extend moving shape datasets with fully controllable new motions. This ability appears to be of great importance with deep learning techniques and the associated need for large learning datasets. In this research direction, we investigate how to create new dynamic scenes using recorded events. More recently, this also includes applying machine learning to datasets of recorded human motions to learn motion spaces that allow to synthesize novel realistic animations.

4.1 4D modeling

Modeling shapes that evolve over time, analyzing and interpreting their motion has been a subject of increasing interest of many research communities including the computer vision, the computer graphics and the medical imaging communities. Recent evolutions in acquisition technologies including 3D depth cameras (Time-of-Flight and Kinect), multi-camera systems, marker based motion capture systems, ultrasound and CT scanners have made those communities consider capturing the real scene and their dynamics, create 4D spatio-temporal models, analyze and interpret them. A number of applications including dense motion capture, dynamic shape modeling and animation, temporally consistent 3D reconstruction, motion analysis and interpretation have therefore emerged.

4.2 Shape Analysis

Most existing shape analysis tools are local, in the sense that they give local insight about an object's geometry or purpose. The use of both geometry and motion cues makes it possible to recover more global information, in order to get extensive knowledge about a shape. For instance, motion can help to decompose a 3D model of a character into semantically significant parts, such as legs, arms, torso and head. Possible applications of such high-level shape understanding include accurate feature computation, comparison between models to detect defects or medical pathologies, and the design of new biometric models.

4.3 Human Motion Analysis

The recovery of dense motion information enables the combined analysis of shapes and their motions. This allows to classify based on motion cues, which can help in the identification of pathologies or the design of new prostheses. Analyzing human motion can also be used to learn generative models of realistic human motion, which is a recent research topic within Morpheo.

5.1 Footprint of research activities

The footprint of our research activity is dominated by dissemination and data costs.

Dissemination strategy: Traditionally, Morpheo's dissemination strategy has been to publish in the top conferences of the field (CVPR, ECCV, ICCV, MICCAI), as well as to give invited talks, leading to some long disance work trips. We currently try to increase submissions to top journals in the field directly, hence reducing travels, while still attending some in-person conferences or seminars to allow for networking.

Data management: The data produced by the Morpheo team occupies large memory volumes. The Kinovis platform typically produces around 1.5GB per second when capturing one actor at 50fps. The platform that also captures X-ray images at CHU produces around 1.3GB of data per second at 30fps for video and X-rays. For practical reasons, we reduce the data as much as possible with respect to the targeted application by only keeping e.g. 3D reconstructions or down-sampled spatial or temporal camera images. Yet, acquiring, processing and storing these data is costly in terms of resources. In addition, the data captured by these platforms are personal data with highly constrained regulations. Our data management therefore needs to consider multiple aspects: data encryption to protect personal information, data backup to allow for reproducibility, and the environmental impact of data storage and processing. For all these aspects, we are constantly checking for new and more satisfactory solutions.

For data processing, we rely heavily on cluster uses, in particular with neural networks which are known to have a heavy carbon footprint. Yet, in our research field, these types of processing methods have been shown to lead to significant performance gains. For this reason, we continue to use neural networks as tools, while attempting to use architectures that allow for faster and more energy efficient training, and simple test cases that can often be trained on local machines (with GPU) to allow for less costly network design. A typical example of this type of research is the work of Briac Toussaint, whose first thesis contribution exhibits a high quality reconstruction algorithm for the Kinovis platform, with under a minute of GPU computation time per frame, an order of magnitude faster than our previous research solution, yet it achieves improved precision.

5.2 Impact of research results

Morpheo's main research topic is not related to sustainability. Yet, some of our research directions could be applied to solve problems relevant to sustainability.

Realistic digital human modeling holds the potential to allow for realistic interactions through large geographic distances, leading to more realistic and satisfactory teleconferencing systems. Morpheo captures and analyzes humans in 4D, thereby allowing to capture both their shapes and movement patterns, and actively works on human body modeling. In this line of work, Morpheo works on the project 3DMOVE to model realistic human motion, and participates in the Nemo.AI joint lab with Interdigital on advancing this topic.

Of course, as with any research direction, ours can also be used to generate technologies that are resource-hungry and whose necessity may be questionable, such as inserting animated 3D avatars in scenarios where simple voice communication would suffice, for instance.

6.1 Awards

• Prix Innovation INRIA: Kinowild
• Marilin Keller (co-supervised by Michael Black and Sergi Pujades) wins Honorable Mention for the 2024 MPI-IS Outstanding Female doctoral Student Prize.
• Best Paper (Nagao Award) at MIRU 2024 - Meeting on Image Recognition and Understanding (Award page), biggest Japanese Symposium on image recognition and understanding. 3D Shape Modeling with Adaptive Centroidal Voronoi Tesselation on Signed Distance Field 13.

6.2 Team Hiring

Jean-Sébastien Franco, Morpheo group leader and previously associate professor, was recruited as full time Senior Inria Researcher on October 1st, 2024.

6.3 CHU collaboration strengthened

• Installation of the Kinovis platform at the CHU

In April 2024, the mobile Kinovis platform was installed in the facilities of the LAGAME laboratory at the Pediatric Hospital in Grenoble (CHUGA), led by Aurélien Courvoisier. The deployment of the platform has allowed to build a first Proof of Concept in which patients with scoliosis are acquired during their regular visits at the hospital. This collaboration aims at better understanding the underlying factors of Adolescent Idiopathic Scoliosis through shape and motion analysis.

6.4 Organization of the IABM conference

The second edition of the Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM 2024) was held in Grenoble on the 25, 26 and 27 March 2024. The objective of this Event is to gather the main actors in France from the academy, industry and the clinics working on Artificial Intelligence applied to medical images. The event is co-organized by the 3IA institures of Grenoble (MIAI), Nice (3IA Côte d’Azur) and Paris (PRAIRIE). The event was a success gathering 240 participants. Sergi Pujades was in the organizing comittee.

7.1 New software

7.2 New platforms

7.3 Open data

8.1 SHOWMe: Robust object-agnostic hand-object 3D reconstruction from RGB video

Participants: Anilkumar Swamy, Vincent Leroy, Philippe Weinzaepfel, Fabien Baradel, Salma Galaaoui, Romain Brégier, Matthieu Armando, Jean-Sébastien Franco, Grégory Rogez.

Show image description

Exerpts of the SHOWME dataset.

In this paper, we tackle the problem of detailed hand-object 3D reconstruction from monocular video with unknown objects, for applications where the required accuracy and level of detail is important, e.g. object hand-over in human-robot collaboration, or manipulation and contact point analysis. While the recent literature on this topic is promising, the accuracy and generalization abilities of existing methods are still lacking. This is due to several limitations, such as the assumption of known object class or model for a small number of instances, or over-reliance on off-the-shelf keypoint and structure-from-motion methods for object-relative viewpoint estimation, prone to complete failure with previously unobserved, poorly textured objects or hand-object occlusions. To address previous method shortcomings, this work presents a 2-stage pipeline superseding state-of-the-art performance on several metrics.

First, the method robustly retrieves viewpoints relying on a learned pairwise camera pose estimator trainable with a low data regime, followed by a globalized Shonan pose averaging. Second, it simultaneously estimates detailed 3D hand-object shapes and refines camera poses using a differential renderer-based optimizer. To better assess the out-of-distribution abilities of existing methods, and to showcase our methodological contributions, this work introduces the new SHOWMe benchmark dataset with 96 sequences annotated with poses, millimetric textured 3D shape scans, and parametric hand models, introducing new object and hand diversity. The work shows that our method is able to reconstruct 100% of these sequences as opposed to SotA Structure-from-Motion (SfM) or hand-keypoint-based pipelines, and obtains reconstructions of equivalent or better precision when existing methods do succeed in providing a result. We hope these contributions lead to further research under harder input assumptions.

This work was published as an article in the Computer Vision and Image Understanding journal 7. The dataset can be downloaded at SHOWME webpage.

8.2 Millimetric Human Surface Capture in Minutes

Participants: Briac Toussaint, Laurence Boissieux, Diego Thomas, Edmond Boyer, Jean-Sébastien Franco.

Show image description

We present an implicit surface reconstruction algorithm achieving millimetric precision in 3 minutes of training time for fast and accurate multi-view human acquisition.

Detailed human surface capture from multiple images is an essential component for many 3D production, analysis and transmission tasks. Yet producing millimetric precision 3D models in practical time, and actually verifying their 3D accuracy in a real-world capture context, remain key challenges due to the lack of specific methods and data for these goals. This work proposes two complementary contributions to this end. The first one is a highly scalable neural surface radiance field approach able to achieve millimetric precision by construction, while demonstrating high compute and memory efficiency. The second one is a novel dataset MVMannequin of clothed mannequin geometry captured with a high resolution hand-held 3D scanner paired with calibrated multi-view images, that allow to verify the millimetric accuracy claim.

Although the proposed approach can produce such a highly dense and precise geometry, the work shows how aggressive sparsification and optimizations of the neural surface pipeline lead to estimations requiring only minutes of computation time and a few GB of VRAM memory on GPU, while allowing for real-time millisecond neural rendering. On the basis of the presented framework and dataset, we provide a thorough experimental analysis of how such accuracies and efficiencies are achieved in the context of multi-camera human acquisition.

The paper was published at the Sigraph Asia 2024 conference program 14, and more resources are provided on the MillimetricHumans project webpage.

8.3 On predicting 3D bone locations inside the human body

Participants: Abdelmoutaleb Dakri, Vaibhav Arora, Léo Challier, Marilyn Keller, Michael Black, Sergi Pujades.

Show image description

In this work we tackle the problem of predicting the location of the bones inside the human body.

Knowing the precise location of the bones inside the human body is key in several medical tasks, such as patient placement inside an imaging device or surgical navigation inside a patient. Our goal is to predict the bone locations using only an external 3D body surface obser- vation. Existing approaches either validate their predictions on 2D data (X-rays) or with pseudo-ground truth computed from motion capture using biomechanical models. Thus, methods either suffer from a 3D-2D projection ambiguity or directly lack validation on clinical imaging data. In this work 9, we start with a dataset of segmented skin and long bones obtained from 3D full body MRI images that we refine into individual bone segmentations. To learn the skin to bones correlations, one needs to register the paired data. Few anatomical models allow to register a skeleton and the skin simultaneously. One such method, SKEL, has a skin and skeleton that is jointly rigged with the same pose parameters. However, it lacks the flexibility to adjust the bone locations inside its skin. To address this, we extend SKEL into SKEL-J to allow its bones to fit the segmented bones while its skin fits the segmented skin. These precise fits allow us to train SKEL-J to more accurately infer the anatomical joint locations from the skin surface. Our qualitative and quantitative results show how our bone location predictions are more accurate than all existing approaches. o foster future research, we make available for research purposes the individual bone segmentations, the fitted SKEL-J models as well as the new inference methods on the project webpage.

8.4 Pose-independent 3D Anthropometry from Sparse Data

Participants: David Bojanić, Stefanie Wuhrer, Tomislav Petković, Tomislav Pribanić.

Show image description

We propose an approach to estimate body measurements from 3D landmark locations on a body in any given pose.

3D digital anthropometry is the study of estimating human body measurements from 3D scans. Precise body measurements are important health indicators in the medical industry, and guiding factors in the fashion, ergonomic and entertainment industries. The measuring protocol consists of scanning the whole subject in the static A-pose, which is maintained without breathing or movement during the scanning process. However, the A-pose is not easy to maintain during the whole scanning process, which can last even up to a couple of minutes. This constraint affects the final quality of the scan, which in turn affects the accuracy of the estimated body measurements obtained from methods that rely on dense geometric data. Additionally, this constraint makes it impossible to develop a digital anthropometry method for subjects unable to assume the A-pose, such as those with injuries or disabilities. We propose a method that can obtain body measurements from sparse land marks acquired in any pose. We make use of the sparse landmarks of the posed subject to create pose-independent features, and train a network to predict the body measurements as taken from the standard A-pose. We show that our method achieves comparable results to competing methods that use dense geometry in the standard A-pose, but has the capability of estimating the body measurements from any pose using sparse landmarks only.

8.5 A Survey on Realistic Virtual Human Animations: Definitions, Features and Evaluations

Participants: Rim Rekik, Stefanie Wuhrer, Ludovic Hoyet, Katja Zibrek, Anne-Hélène Olivier.

Show image description

The central goal of this survey is to explore the aspects of animation realism of virtual humans.

Generating realistic animated virtual humans is a problem that has been extensively studied with many applications in different types of virtual environments. However, the creation process of such realistic animations is challenging, especially because of the number and variety of influencing factors, that should then be identified and evaluated. In this paper, we attempt to provide a clearer understanding of how the multiple factors that have been studied in the literature impact the level of realism of animated virtual humans, by providing a survey of studies assessing their realism. This includes a review of features that have been manipulated to increase the realism of virtual humans, as well as evaluation approaches that have been developed. As the challenges of evaluating animated virtual humans in a way that agrees with human perception are still active research problems, this survey further identifies important open problems and directions for future research.

8.6 Correspondence-free online human motion retargeting

Participants: Rim Rekik, Mathieu Marsot, Anne-Hélène Olivier, Jean-Sébastien Franco, Stefanie Wuhrer.

Show image description

This work achieves high-quality motion retargeting between unregistered models, and can be used on the output of the Kinovis platform directly.

We present a data-driven framework for unsupervised human motion retargeting that animates a target subject with the motion of a source subject. Our method is correspondence-free, requiring neither spatial correspondences between the source and target shapes nor temporal correspondences between different frames of the source motion. This allows to animate a target shape with arbitrary sequences of humans in motion, possibly captured using 4D acquisition platforms or consumer devices. Our method unifies the advantages of two existing lines of work, namely skeletal motion retargeting, which leverages long-term temporal context, and surface-based retargeting, which preserves surface details, by combining a geometry-aware deformation model with a skeleton-aware motion transfer approach. This allows to take into account long-term temporal context while accounting for surface details. During inference, our method runs online, i.e. input can be processed in a serial way, and retargeting is performed in a single forward pass per frame. Experiments show that including long-term temporal context during training improves the method’s accuracy for skeletal motion and detail preservation. Furthermore, our method generalizes to unobserved motions and body shapes. We demonstrate that our method achieves state-of-the-art results on two test datasets and that it can be used to animate human models with the output of a multi-view acquisition platform.

8.7 3D Shape Modeling with Adaptive Centroidal Voronoi Tesselation on Signed Distance Field

Participants: Diego Thomas, Briac Toussaint, Jean-Sébastien Franco, Edmond Boyer.

Show image description

Sampling is shown, left figure shows the reconstructed geometry, right figure a color-coded 3D model

Volumetric shape representations have become ubiquitous in multi-view reconstruction tasks. They often build on regular voxel grids as discrete representations of 3D shape functions, such as SDF or radiance fields, either as the full shape model or as sampled instantiations of continuous representations, as with neural networks. Despite their proven efficiency, voxel representations come with the precision versus complexity trade-off. This inherent limitation can significantly impact performance when moving away from simple and uncluttered scenes.

This work investigates an alternative discretization strategy with the Centroidal Voronoi Tessellation (CVT). CVTs allow to better partition the observation space with respect to shape occupancy and to focus the discretization around shape surfaces. To leverage this discretization strategy for multi-view reconstruction, this work introduced a volumetric optimization framework that combines explicit SDF fields with a shallow color network, in order to estimate 3D shape properties over tetrahedral grids. Experimental results with Chamfer statistics validate this approach with unprecedented reconstruction quality on various scenarios such as objects, open scenes or human.

This work has lead to several publicatons, a research report 16, and a MIRU 2024 Japanese computer vision conference, where is obtained a best paper award 13. It is also the subject of an accepted submission to the WACV 2025 computer vision conference.

8.8 HIT: Human Implicit Tissues

Participants: Marilyn Keller, Vaibhav Arora, Abdelmoutaleb Dakri, Shivam Chandhok, Jürgen Machann, Andreas Fritsche, Michael J. Black, Sergi Pujades.

Show image description

In this work we learn how to predict the volumetric internal tissues from a person from the shape of its body surface.

The creation of personalized anatomical digital twins is important in the fields of medicine, computer graphics, sports science, and biomechanics. To observe a subject's anatomy, expensive medical devices (MRI or CT) are required and the creation of the digital model is often time-consuming and involves manual effort. Instead, we leverage the fact that the shape of the body surface is correlated with the internal anatomy; for example, from surface observations alone, one can predict body composition and skeletal structure. In this work 10, we go further and learn to infer the 3D location of three important anatomic tissues: subcutaneous adipose tissue (fat), lean tissue (muscles and organs), and long bones. To learn to infer these tissues, we tackle several key challenges. We first create a dataset of human tissues by segmenting full-body MRI scans and registering the SMPL body mesh to the body surface. With this dataset, we train HIT (Human Implicit Tissues), an implicit function that, given a point inside a body, predicts its tissue class. HIT leverages the SMPL body model shape and pose parameters to canonicalize the medical data. Unlike SMPL, which is trained from upright 3D scans, the MRI scans are taken of subjects lying on a table, resulting in significant soft-tissue deformation. Consequently, HIT uses a learned volumetric deformation field that undoes these deformations. Since HIT is parameterized by SMPL, we can repose bodies or change the shape of subjects and the internal structures deform appropriately. We perform extensive experiments to validate HIT's ability to predict plausible internal structure for novel subjects. The dataset and HIT model are available on the HIT project webpage to foster future research in this direction.

8.9 OSATTA

Participants: Felix Kuper, Sergi Pujades.

Show image description

In this work we learn how to find image transformation families which can correct for the individual bias present in a small cohort of data. These transformations align the source data to the target Fundamental Model distribution to improve its performance.

Fundamental models (FM) are reshaping the research paradigm by providing ready-to-use solutions to many challenging tasks, such as image classification, registration, or segmentation. Yet, their performance on new dataset cohorts significantly drops, particularly due to domain gaps between the training (source) and testing (target) data. Recently, test time augmentation strategies aim at finding target-to-source- mappings (t2sm), which improve the performance of the FM on the target dataset, by inspecting the the FM weights, thus assuming access to them. While this assumption holds for open research models, it does not for commercial ones (e.g., Chat-GPT). These are provided as black boxes, and the training data and the model weights are not available. In our work11, we propose a new generic few-shot method that enables the computation of a target-to-source mapping by only using the black-box model’s outputs. We start by defining a parametric family of functions for the t2sm, and with a simple loss function, we optimize the t2sm parameters based on a single labeled image volume. This effectively provides a mapping between the source domain and the target domain. In our experiments, we investigate how to improve the segmentation performance of a given FM (a UNet), and we outperform state-of-the-art accuracy in the 1-shot setting, with further improvement in a few-shot setting. Our approach is invariant to the model architecture as the FM is treated as a black box, which significantly increases our method’s practical utility in real-world scenarios. We make the code available for reproducibility purposes on the OSATTA project webpage.

9.1 Bilateral contracts with industry

Participants: Antoine Dumoulin, Youssef Ben Cheikh, Stefanie Wuhrer, Jean-Sébastien Franco.

• The Morpheo INRIA team has a collaboration with Interdigital in Rennes through the Nemo.AI joint lab. The kickoff to this collaboration has happened in November 2022. The collaboration involves two PhD co-supervisions, Antoine Dumoulin and Youssef Ben Cheikh, at the INRIA de l'Université Grenoble Alpes. The subject of the collaboration revolves around digital humans, one the one hand to estimate the clothing of humans from images, and on the other to estimate hair models from one or several videos.

9.2 Bilateral grants with industry

Participants: Jean-Sébastien Franco.

1. The Morpheo team is also involved in a CIFRE PhD co-supervision (Anilkumar Swamy) with Naver Labs since July 2021, with Gregory Rogez, Mathieu Armando and Vincent Leroy. The work revolves around monocular hand-object reconstruction by showing the object to the camera.

10.1 International initiatives

10.2 International research visitors

10.3 National initiatives

10.4 Regional initiatives

11.1 Promoting scientific activities

11.2 Teaching - Supervision - Juries

11.3 Popularization

12.1 Major publications

• 1 inproceedingsA.Abdelmouttaleb Dakri, V.Vaibhav Arora, L.Léo Challier, M.Marilyn Keller, M. J.Michael J Black and S.Sergi Pujades. On predicting 3D bone locations inside the human body.MICCAI 2024 - 27th International Conference on Medical Image Computing and Computer Assisted InterventionMarrakech, Morocco2024, 1-11HAL
• 2 inproceedingsM.Marilyn Keller, V.Vaibhav Arora, A.Abdelmouttaleb Dakri, S.Shivam Chandhok, J.Jürgen Machann, A.Andreas Fritsche, M.Michael Black and S.Sergi Pujades. HIT: Estimating Internal Human Implicit Tissues from the Body Surface.CVPR 2024 - IEEE/CVF Conference on Computer Vision and Pattern RecognitionLecture Notes in Computer ScienceSeattle, United StatesIEEE2024, 3480-3490HALDOI
• 3 articleR.Rim Rekik, S.Stefanie Wuhrer, L.Ludovic Hoyet, K.Katja Zibrek and A.-H.Anne-Hélène Olivier. A Survey on Realistic Virtual Human Animations: Definitions, Features and Evaluations.Computer Graphics Forum432May 2024, e15064:1-21HALDOI
• 4 articleA.Anilkumar Swamy, V.Vincent Leroy, P.Philippe Weinzaepfel, F.Fabien Baradel, S.Salma Galaaoui, R.Romain Brégier, M.Matthieu Armando, J.-S.Jean-Sébastien Franco and G.Grégory Rogez. SHOWMe: Robust object-agnostic hand-object 3D reconstruction from RGB video.Computer Vision and Image Understanding247October 2024, 104073HALDOI
• 5 inproceedingsB.Briac Toussaint, L.Laurence Boissieux, D.Diego Thomas, E.Edmond Boyer and F.Franco Jean-Sébastien. Millimetric Human Surface Capture in Minutes.SIGGRAPH Asia 2024 - 17th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in AsiaTokyo, JapanACM2024, 1-12HALDOI

12.2 Publications of the year