{"ID":6528863,"CreatedAt":"2026-07-13T19:33:24.193068023Z","UpdatedAt":"2026-07-13T19:33:24.193068023Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.14841","arxiv_id":"2606.14841","title":"Multi-HMR 2: Multi-Person Camera-Centric Human Detection, Mesh Recovery and Tracking","abstract":"Most advances in human mesh recovery (HMR) have focused on pelvis-centered recovery, overlooking metric 3D localization and detection accuracy in the camera coordinate system - two key factors for real-world applications such as human-robot interaction and social scene understanding. Current evaluation protocols often ignore these aspects, emphasizing per-person, root-centered recovery rather than camera-space perception. As a result, existing approaches rely on fixed camera assumptions or handcrafted post-processing, limiting their robustness and practical deployment. We introduce Multi-HMR 2, a simple yet robust DETR-based framework for Multi-person Camera-centric Human detection, mesh Recovery, and tracking. Multi-HMR 2 predicts a scene-consistent camera together with human meshes, enabling metric 3D localization without ground-truth intrinsics. Moreover, by distilling image-based memory features from SAM2, Multi-HMR 2 extends to tracking, achieving consistent identity association without video supervision. Despite its conceptual simplicity - no handcrafted components, no video input, and no ground-truth cameras - Multi-HMR 2 achieves state-of-the-art pelvis-centered performance while substantially improving detection accuracy and metric 3D localization.","url_abs":"https://arxiv.org/abs/2606.14841v1","url_pdf":"https://arxiv.org/pdf/2606.14841v1","authors":"Guénolé Fiche, Philippe Weinzaepfel, Romain Brégier, Fabien Baradel","published":"2026-06-12T17:23:42Z","has_code":false}