BioPose: Advancing Biomechanically-Accurate 3D Pose Estimation – Accepted at WACV 2025

We are pleased to announce that our paper, “BioPose: Biomechanically-Accurate 3D Pose Estimation from Monocular Videos,” has been accepted for presentation at WACV 2025. This work introduces a novel framework for biomechanically accurate 3D human pose estimation, bridging the gap between learning-based methods and physics-based motion analysis.

Advancing 3D Pose Estimation for Real-World Applications

Conventional 3D pose estimation methods struggle with biomechanical plausibility, limiting their applications in biomechanics, healthcare, rehabilitation, and robotics. While marker-based motion capture systems provide high accuracy, they are often costly and impractical for many real-world scenarios. BioPose addresses this challenge by introducing a learning-based framework that ensures biomechanical feasibility while achieving state-of-the-art (SOTA) performance.

Key Contributions of BioPose:

• Multi-Query Human Mesh Recovery (MQ-HMR): A novel multi-scale deformable transformer that enhances the accuracy of 3D human mesh reconstruction.
• 2D-Informed Pose Refinement: Aligns 3D pose estimates with 2D cues, ensuring improved consistency and anatomical accuracy.
• Neural Inverse Kinematics (NeurIK): A spatial-temporal network that leverages mesh vertices as virtual markers, applying biomechanics-guided constraints to refine human joint movements.

This research presents a significant step forward in AI-driven human motion analysis, with potential applications in clinical motion assessment, sports science, and human-computer interaction.

Acknowledgments

We extend our gratitude to the research team, Farnoosh Koleini, Pu Wang, Hongfei Xue, Ahmed Helmy, and Abbey Fenwick, for their contributions to this work.

📄 Read more about BioPose: Publication Link

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