Physics Informed Human Posture Estimation Based on 3D Landmarks from Monocular RGB-Videos

• URL: http://arxiv.org/abs/2512.06783v1
• Date: Sun, 07 Dec 2025 10:54:09 GMT
• Title: Physics Informed Human Posture Estimation Based on 3D Landmarks from Monocular RGB-Videos
• Authors: Tobias Leuthold, Michele Xiloyannis, Yves Zimmermann,
• Abstract summary: State-of-the-art models like BlazePose excel in real-time pose tracking, but their lack of anatomical constraints indicates improvement potential by including physical knowledge.<n>We present a real-time post-processing algorithm fusing the strengths of BlazePose 3D and 2D estimations using a weighted optimization.<n> Evaluation using the Physio2.2M dataset shows a 10.2 percent reduction in 3D MPJPE and a 16.6 percent decrease in errors of angles between body segments compared to BlazePose 3D estimation.
• Score: 1.3399128310861848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Applications providing automated coaching for physical training are increasing in popularity, for example physical therapy. These applications rely on accurate and robust pose estimation using monocular video streams. State-of-the-art models like BlazePose excel in real-time pose tracking, but their lack of anatomical constraints indicates improvement potential by including physical knowledge. We present a real-time post-processing algorithm fusing the strengths of BlazePose 3D and 2D estimations using a weighted optimization, penalizing deviations from expected bone length and biomechanical models. Bone length estimations are refined to the individual anatomy using a Kalman filter with adapting measurement trust. Evaluation using the Physio2.2M dataset shows a 10.2 percent reduction in 3D MPJPE and a 16.6 percent decrease in errors of angles between body segments compared to BlazePose 3D estimation. Our method provides a robust, anatomically consistent pose estimation based on a computationally efficient video-to-3D pose estimation, suitable for automated physiotherapy, healthcare, and sports coaching on consumer-level laptops and mobile devices. The refinement runs on the backend with anonymized data only.

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