Internship - 3D Infant Motion Estimation from RGB Video

Internship - 3D Infant Motion Estimation from RGB Video Download job offer in PDF format Contract type : Internship agreement Level of qualifications required : Master's or equivalent Fonction : Internship Research About the research centre or Inria department 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. Context 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. Assignment 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] SAM 3D Body: Robust Full-Body Human Mesh Recovery, Yang et al., CVPR 2026 [2] BEDLAM2.0: Synthetic Humans and Cameras in Motion, Tesch et al., NeurIPS 2025 (Datasets and Benchmarks) [3] Cross-Model Distillation of a Human-Pose Foundation Model from Unannotated Infant Video for Markerless 3D Pose Estimation, Cotton et al., 2026 (arXiv preprint) Main activities • 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 Skills 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. Benefits package • 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 Remuneration Minimum legal gratification Apply for this position Share • Facebook • Linkedin • Twitter • Email General Information • Theme/Domain : Vision, perception and multimedia interpretation Scientific computing (BAP E) • Town/city : Montbonnot • Inria Center : Centre Inria de l'Université Grenoble Alpes • Starting date : 2027-02-01 • Duration of contract : 5 months • Deadline to apply : 2026-12-05 Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed. Instruction to apply Applications must be submitted online on the Inria website. Processing of applications sent by other channels is not guaranteed. Your application file must include a CV, covering letter, academic transcripts and course syllabus for the last two years of the program followed Please include with your application any published documents to which you have contributed (as a co-author): master's thesis, scientific publication, etc. Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment. Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities. Contacts • Inria Team : KINETIX • Recruiter : Pujades Sergi / sergi.pujades-rocamora@inria.fr The keys to success Only complete applications will be considered. Please send: • CV • Cover letter • academic transcripts • if possible, name of 1-2 professors or supervisors who are willing to recommend your application About Inria Inria, the French national institute for research in digital science and technology, supports the French government in national research and innovation strategies in the digital field, acting as Digital Programs Agency. Inria leads over 300 research and innovation projects with its 3,500 scientists, engineers, and support staff, in partnership with universities and the digital ecosystem (businesses, entrepreneurs, and public stakeholders). Together, we explore strategic fields such as artificial intelligence, cybersecurity, quantum computing, cloud technologies, digital transformation in healthcare, digital twins, and digital technologies for defence. We develop practical solutions such as software, tech startups, partnerships with national companies, and cutting-edge training programmes. Our goal is to drive scientific, technological, and industrial excellence to ensure France’s digital sovereignty.