Internship - 3D Infant Motion Estimation From Rgb Video H/F - INRIA

A propos d'Inria

Inria, l'institut national de recherche dans les sciences et technologies du numérique, est en appui de l'État pour les stratégies nationales de recherche et d'innovation du numérique en tant qu'Agence de programmes. Inria mène plus de 300 projets de recherche et d'innovation avec ses 3500 scientifiques, ingénieurs et personnels d'appui, en partenariat avec les universités et l'écosystème numérique (entreprises, entrepreneurs, acteurs publics). Ensemble, nous explorons des domaines clés comme l'intelligence artificielle, la cybersécurité, l'informatique quantique, le Cloud, la transformation numérique de la santé, les jumeaux numériques ou encore les technologies numériques pour la défense. Nous construisons des solutions concrètes telles que des logiciels, des startups technologiques, des partenariats avec les entreprises du tissu national et des formations de pointe. Notre objectif : l'impact scientifique, technologique et industriel au service de la souveraineté numérique de la France.

Internship - 3D Infant Motion Estimation from RGB Video
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : Convention de stage

Niveau de diplôme exigé : Bac +4 ou équivalent

Fonction : Stagiaire de la recherche

A propos du centre ou de la direction fonctionnelle

The Inria Grenoble research center groups together almost 600 people in 27 research teams and 8 research support departments.

Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (University Grenoble Alpes, CNRS, CEA, INRAE, ...), but also with key economic players in the area.

Inria Grenoble is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.

Contexte et atouts du poste

The internship will take place within the Kinetix team at Inria of Grenoble Alpes University. The student will be supervised by Felix Küper, Nikolas Hesse, and Sergi Pujades.

Mission confiée

Context

Recovering 3D human pose and shape from images or videos is a core problem in 3D vision. Recent foundation models for human mesh recovery (HMR), such as SAM 3D Body [1], estimate full-body meshes from a single image with impressive robustness. Video-based methods add temporal consistency and handle camera motion. Large synthetic datasets such as BEDLAM2 [2] have been key to training them, because they provide perfect 3D ground truth.

These models are trained almost exclusively on adults. Infants have different body proportions, poses, and appearance, and marker-based ground truth cannot be collected for them, so labeled data is scarce. Yet infant motion analysis has strong clinical value. The quality of spontaneous movement in the first months of life is an early indicator of neuromotor disorders such as cerebral palsy, and video-based markerless capture would make assessment possible at scale. Recent work [3] shows that fine-tuning SAM 3D Body on unlabeled infant video improves its predictions. However, it relies on pseudo-labels from another model, operates on single frames, and still reconstructs infants with an adult-shaped body model.

Internship

The goal of this internship is to improve the estimation of 3D infant motion from RGB video.

The work will include:

- running and benchmarking state-of-the-art HMR methods, image-based and video-based, on infant videos, and analyzing their failure modes;
- creating a large-scale synthetic video dataset of infants with exact 3D ground truth, as an important milestone toward fine-tuning. To our knowledge, no such dataset exists;
- running, fine-tuning, and improving video-based HMR methods, and evaluating the gains on real infant videos.

Dataset generation and training will run on the Kinovis and GRICAD compute cluster.

The project combines 3D vision, synthetic data generation, deep learning, and large-scale experiments on an HPC cluster. A scientific publication is a realistic outcome.

What you will gain

- Hands-on work with current frontier models
- Experience in 3D vision and synthetic data generation
- Experience with HPC
- A real application with clinical impact
- A potential publication

References

[1], Yang et al., CVPR 2026

[2], Tesch et al., NeurIPS 2025 (Datasets and Benchmarks)

[3], Cotton et al., 2026 (arXiv preprint)

Principales activités

- Reading and analyzing advantages and disadvantages of related literature
- Studying and preparing data
- Implementing solutions for the problem
- Evaluating the implemented solutions and existing works

Compétences

Profile

We are looking for a highly motivated M2 student in Computer Science, Computer Vision, Machine Learning, Computer Graphics, Applied Mathematics, or a related field. Good programming skills in Python are required, as is experience with deep learning frameworks (PyTorch). Background in computer vision, 3D geometry, rendering, or human body models (SMPL-type) would be appreciated.

Avantages

- Subsidized meals
- Partial reimbursement of public transport costs
- Possibility of teleworking
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities

Rémunération

Minimum legal gratification

• Python
• Computer vision
• Machine learning