End-to-End Motion Capture from Rigid Body Markers with Geodesic Loss

• URL: http://arxiv.org/abs/2511.16418v1
• Date: Thu, 20 Nov 2025 14:43:05 GMT
• Title: End-to-End Motion Capture from Rigid Body Markers with Geodesic Loss
• Authors: Hai Lan, Zongyan Li, Jianmin Hu, Jialing Yang, Houde Dai,
• Abstract summary: Marker-based optical motion capture (MoCap) faces practical challenges, such as time-consuming preparation and marker identification ambiguity.<n>We introduce a novel fundamental unit for MoCap, the Rigid Body Marker (RBM), which provides unambiguous 6-DoF data.<n>We develop a deep-learning-based regression model that directly estimates SMPL parameters under a geodesic loss.
• Score: 3.8338194488710453
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Marker-based optical motion capture (MoCap), while long regarded as the gold standard for accuracy, faces practical challenges, such as time-consuming preparation and marker identification ambiguity, due to its reliance on dense marker configurations, which fundamentally limit its scalability. To address this, we introduce a novel fundamental unit for MoCap, the Rigid Body Marker (RBM), which provides unambiguous 6-DoF data and drastically simplifies setup. Leveraging this new data modality, we develop a deep-learning-based regression model that directly estimates SMPL parameters under a geodesic loss. This end-to-end approach matches the performance of optimization-based methods while requiring over an order of magnitude less computation. Trained on synthesized data from the AMASS dataset, our end-to-end model achieves state-of-the-art accuracy in body pose estimation. Real-world data captured using a Vicon optical tracking system further demonstrates the practical viability of our approach. Overall, the results show that combining sparse 6-DoF RBM with a manifold-aware geodesic loss yields a practical and high-fidelity solution for real-time MoCap in graphics, virtual reality, and biomechanics.

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