Internship - 3D Infant Motion Estimation from RGB Video

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description . 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. 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 e