Biomechanically Grounded 3D Human Mesh Reconstruction via Spatio-Temporal Adapters

Recovering 3D human pose and shape from monocular input is central to computer vision, yet for anatomy-aware applications such as clinical motion analysis and sports biomechanics, geometric accuracy alone is insufficient: reconstructions must also be biomechanically valid. Most methods build on SMPL, whose joints are unconstrained ball-and-sockets and often produce physically impossible rotations. The SKEL model instead provides a physiologically grounded skeleton that is valid by construction, but learning to predict SKEL has remained limited by the scarcity of SKEL annotations. We introduce BioHMR, a SKEL-based framework that is simultaneously scalable, temporally coherent, and efficient. BioHMR reconstructs in an explicit skeletal latent space: a compact set of learnable joint tokens, one per SKEL joint, gathers evidence from image patches, reasons over the SKEL kinematic tree, and tracks each joint through time within a Vision Transformer, giving temporally coherent reconstruction without dense spatio-temporal attention. To overcome the annotation scarcity, a lightweight SMPL-to-SKEL mapper, trained by mesh consistency, generates pseudo-SKEL supervision from abundant SMPL annotations roughly $300\times$ faster than prior conversion, making large-scale SKEL training practical. The same joint-token attention drives an optional pose-aware patch pruning for a tunable compute-accuracy trade-off at inference. Across COCO, LSP-Extended, 3DPW, and MOYO, BioHMR attains state-of-the-art accuracy, efficiency, and temporal coherence among SKEL-based methods, with the lowest MPJPE on both 3DPW and MOYO and 2D accuracy on par with the strongest SMPL-based baselines. Moreover, its reconstructions stay biomechanically valid by construction, cutting per-joint violation rates on MOYO from 44–56% for SMPL-based HMR2.0 to 0–4%.