Data-driven character animation based on motion capture can produce highly
naturalistic behaviors and, when combined with physics simulation, can provide
for natural procedural responses to physical perturbations, environmental
changes, and morphological discrepancies. Motion capture remains the most
popular source of motion data, but collecting mocap data typically requires
heavily instrumented environments and actors. In this paper, we propose a
method that enables physically simulated characters to learn skills from videos
(SFV). Our approach, based on deep pose estimation and deep reinforcement
learning, allows data-driven animation to leverage the abundance of publicly
available video clips from the web, such as those from YouTube. This has the
potential to enable fast and easy design of character controllers simply by
querying for video recordings of the desired behavior. The resulting
controllers are robust to perturbations, can be adapted to new settings, can
perform basic object interactions, and can be retargeted to new morphologies
via reinforcement learning. We further demonstrate that our method can predict
potential human motions from still images, by forward simulation of learned
controllers initialized from the observed pose. Our framework is able to learn
a broad range of dynamic skills, including locomotion, acrobatics, and martial
arts.

本研究提出了一种基于深度姿态估计和深度强化学习的方法，使得物理模拟的角色能够从公开视频片段中学习技能，并提供了一个快速设计角色控制器的方法，结果是鲁棒的，并能够适应新的环境并预测人类动作.