Existing deep models predict 2D and 3D kinematic poses from video that are
approximately accurate, but contain visible errors that violate physical
constraints, such as feet penetrating the ground and bodies leaning at extreme
angles. In this paper, we present a physics-based method for inferring 3D human
motion from video sequences that takes initial 2D and 3D pose estimates as
input. We first estimate ground contact timings with a novel prediction network
which is trained without hand-labeled data. A physics-based trajectory
optimization then solves for a physically-plausible motion, based on the
inputs. We show this process produces motions that are significantly more
realistic than those from purely kinematic methods, substantially improving
quantitative measures of both kinematic and dynamic plausibility. We
demonstrate our method on character animation and pose estimation tasks on
dynamic motions of dancing and sports with complex contact patterns.

本文介绍了一种基于物理模型的方法，用于从视频中推断 3D 人体运动，该方法利用初始的 2D 和 3D 姿态估计作为输入，并通过物理轨迹优化得出一个符合物理规律的运动。实验结果显示，相较于纯运动学方法，该方法能够显著地提高动作的真实性和准确性。