BioPose: Biomechanically-accurate 3D Pose Estimation from Monocular Videos
Farnoosh Koleini, Muhammad Usama Saleem, Pu Wang, Hongfei Xue, Ahmed, Helmy, Abbey Fenwick

TL;DR
BioPose is a learning-based framework that predicts biomechanically accurate 3D human poses from monocular videos, overcoming limitations of parametric models and costly motion capture systems.
Contribution
It introduces a novel multi-component framework combining mesh recovery, inverse kinematics, and 2D refinement for accurate biomechanical pose estimation from monocular videos.
Findings
Outperforms state-of-the-art methods on benchmark datasets
Achieves high accuracy in joint localization and movement capture
Demonstrates applicability in biomechanics, healthcare, and robotics
Abstract
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…
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