用物理规律从动作捕捉数据推算人体地面反作用力，无需力板。
Physics-informed Ground Reaction Dynamics from Human Motion Capture
- 基于欧拉积分与PD算法，结合物理约束计算地面反作用力。
- 在GroundLink数据集上，力估计精度和根轨迹仿真精度均优于基线。
- 适合运动分析、生物力学等领域，尤其适用于无力板场景。
人体动力学是生物力学、运动科学、计算机视觉与图形学等重要研究领域的关键信息。现有方法通过力板采集外部反作用力，并与动作捕捉数据同步，利用黑箱深度学习模型进行动力学估计。然而，力板为专用设备，仅限实验室使用，严重限制了人体动力学的学习。为此，我们提出一种新方法，直接从更可靠的动作捕捉数据中估算人体地面反作用动力学，以物理定律和计算模拟为约束。该方法采用高精度的欧拉积分方案与PD控制算法，计算地面反作用力。所生成的物理约束力用于指导学习模型，提升动力学估计准确性。在GroundLink数据集上的实验表明，该方法在：1）与力板测量对比的地面反作用力估计精度；2）模拟的根轨迹精度方面，均优于基线模型。代码已公开于https://github.com/cuongle1206/Phys-GRD。
原文摘要 · Abstract (English)
Body dynamics are crucial information for the analysis of human motions in important research fields, ranging from biomechanics, sports science to computer vision and graphics. Modern approaches collect the body dynamics, external reactive force specifically, via force plates, synchronizing with human motion capture data, and learn to estimate the dynamics from a black-box deep learning model. Being specialized devices, force plates can only be installed in laboratory setups, imposing a significant limitation on the learning of human dynamics. To this end, we propose a novel method for estimating human ground reaction dynamics directly from the more reliable motion capture data with physics laws and computational simulation as constrains. We introduce a highly accurate and robust method for computing ground reaction forces from motion capture data using Euler's integration scheme and PD algorithm. The physics-based reactive forces are used to inform the learning model about the physics-informed motion dynamics thus improving the estimation accuracy. The proposed approach was tested on the GroundLink dataset, outperforming the baseline model on: 1) the ground reaction force estimation accuracy compared to the force plates measurement; and 2) our simulated root trajectory precision. The implementation code is available at https://github.com/cuongle1206/Phys-GRD
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