用稀疏刚体标记实现端到端高精度动作捕捉，计算量大幅降低。
End-to-End Motion Capture from Rigid Body Markers with Geodesic Loss
- 引入刚体标记（RBM）提供明确6自由度数据，简化采集流程
- 基于地几何损失的深度学习模型，端到端估计SMPL参数，精度达当前最优
- 适合实时图形、虚拟现实与生物力学场景，实测验证可行性
基于标记的光学动作捕捉（MoCap）虽为精度标杆，但依赖密集标记导致准备耗时且标记识别模糊，制约其可扩展性。为此，本文提出一种新基础单元——刚体标记（RBM），可提供明确的6自由度数据，显著简化部署流程。基于此新模态，我们构建了端到端的深度学习回归模型，直接估计SMPL参数，采用流形感知的地几何损失。该方法在计算量上比优化方法少一个数量级，性能相当。模型在AMASS合成数据上训练，实现当前最佳的姿态估计精度；真实世界中使用Vicon系统采集的数据进一步验证了其实际可用性。结果表明，结合稀疏6-DoF RBM与地几何损失，可实现高保真、实时的动作捕捉解决方案，适用于图形学、虚拟现实与生物力学领域。
原文摘要 · Abstract (English)
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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