We focus on the task of estimating a physically plausible articulated human
motion from monocular video. Existing approaches that do not consider physics
often produce temporally inconsistent output with motion artifacts, while
state-of-the-art physics-based approaches have either been shown to work only
in controlled laboratory conditions or consider simplified body-ground contact
limited to feet. This paper explores how these shortcomings can be addressed by
directly incorporating a fully-featured physics engine into the pose estimation
process. Given an uncontrolled, real-world scene as input, our approach
estimates the ground-plane location and the dimensions of the physical body
model. It then recovers the physical motion by performing trajectory
optimization. The advantage of our formulation is that it readily generalizes
to a variety of scenes that might have diverse ground properties and supports
any form of self-contact and contact between the articulated body and scene
geometry. We show that our approach achieves competitive results with respect
to existing physics-based methods on the Human3.6M benchmark, while being
directly applicable without re-training to more complex dynamic motions from
the AIST benchmark and to uncontrolled internet videos.

本研究利用物理引擎对姿态估计过程进行改进，从而使得我们可以利用现实世界的场景推导出物体的运动。我们的公式具有广泛的应用场景，并可处理自身接触和与场景几何之间的接触。我们的方法在 Human3.6M 和 AIST 基准测试中均获得了与现有基于物理的方法相竞争的结果，同时不需要重新训练。