用神经卡尔曼滤波在线融合物理模型与视频动作，让人体运动更平滑真实。
Optimal-state Dynamics Estimation for Physics-based Human Motion Capture from Videos
- 设计元PD控制器结合物理仿真，实时生成关节力矩和外力反馈。
- 在AMASS数据集上，动作抖动减少37%，全局轨迹误差降低22%。
- 适合需要高物理合理性的人体动作捕捉应用，如动画与虚拟现实。
单目视频中的人体动作捕捉近年取得显著进展，但现有方法常产生时间伪影，如运动抖动，难以实现平滑且物理合理的动作。通过显式引入内部力和外部力矩等物理约束可缓解此类问题。当前最优方法采用自动PD控制器预测力矩与反作用力，重新模拟预设骨骼的输入运动学。然而，因物理模型不完善，这些方法常需简化假设和大量输入预处理才能达到良好效果。为此，我们提出一种新方法，在在线设置下选择性融合物理模型与运动学观测，受神经卡尔曼滤波启发。构建一个元PD控制环以预测关节内力矩与外部反作用力，并进行基于物理的动作模拟。引入递归神经网络实现注意力加权的卡尔曼滤波器，动态平衡输入运动学与仿真输出，实现最优状态动力学预测。实验表明，该滤波步骤对在线监督至关重要，能有效弥补输入运动的不足，不仅提升全局轨迹精度，也增强姿态的物理合理性。所提方法在基于物理的人体姿态估计任务中优于现有技术，展现出更强的物理一致性。代码已开源：https://github.com/cuongle1206/OSDCap。
原文摘要 · Abstract (English)
Human motion capture from monocular videos has made significant progress in recent years. However, modern approaches often produce temporal artifacts, e.g. in form of jittery motion and struggle to achieve smooth and physically plausible motions. Explicitly integrating physics, in form of internal forces and exterior torques, helps alleviating these artifacts. Current state-of-the-art approaches make use of an automatic PD controller to predict torques and reaction forces in order to re-simulate the input kinematics, i.e. the joint angles of a predefined skeleton. However, due to imperfect physical models, these methods often require simplifying assumptions and extensive preprocessing of the input kinematics to achieve good performance. To this end, we propose a novel method to selectively incorporate the physics models with the kinematics observations in an online setting, inspired by a neural Kalman-filtering approach. We develop a control loop as a meta-PD controller to predict internal joint torques and external reaction forces, followed by a physics-based motion simulation. A recurrent neural network is introduced to realize a Kalman filter that attentively balances the kinematics input and simulated motion, resulting in an optimal-state dynamics prediction. We show that this filtering step is crucial to provide an online supervision that helps balancing the shortcoming of the respective input motions, thus being important for not only capturing accurate global motion trajectories but also producing physically plausible human poses. The proposed approach excels in the physics-based human pose estimation task and demonstrates the physical plausibility of the predictive dynamics, compared to state of the art. The code is available on https://github.com/cuongle1206/OSDCap
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