BioPose: Biomechanically-Accurate 3D Pose Estimation from Monocular Videos

Farnoosh Koleini, Muhammad Usama Saleem, Pu Wang, Hongfei Xue, Ahmed Helmy, Abbey Fenwick; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 6330-6339

Recent advancements in 3D human pose estimation from single-camera images and videos have relied on parametric models like SMPL. However these models oversimplify anatomical structures limiting their accuracy in capturing true joint locations and movements which reduces their applicability in biomechanics healthcare and robotics. Biomechanically accurate pose estimation on the other hand typically requires costly marker-based motion capture systems and optimization techniques in specialized labs. To bridge this gap we propose BioPose a novel learning-based framework for predicting biomechanically accurate 3D human pose directly from monocular videos. BioPose includes three key components: a multi-query human mesh recovery model (MQ-HMR) a neural inverse kinematics (NeurIK) model and a 2D-informed pose refinement technique. MQ-HMR leverages a multi-query deformable transformer to extract multi-scale fine-grained image features enabling precise human mesh recovery. NeurIK treats the mesh vertices as virtual markers applying a spatial-temporal network to regress biomechanically accurate 3D poses under anatomical constraints. To further improve 3D pose estimations a 2D-informed refinement step optimizes the query tokens during inference by aligning the 3D structure with 2D pose observations. Experiments on benchmark datasets demonstrate that BioPose significantly outperforms state-of-the-art methods.