This paper introduces a motion retargeting method that preserves
self-contacts and prevents interpenetration. Self-contacts, such as when hands
touch each other or the torso or the head, are important attributes of human
body language and dynamics, yet existing methods do not model or preserve these
contacts. Likewise, interpenetration, such as a hand passing into the torso,
are a typical artifact of motion estimation methods. The input to our method is
a human motion sequence and a target skeleton and character geometry. The
method identifies self-contacts and ground contacts in the input motion, and
optimizes the motion to apply to the output skeleton, while preserving these
contacts and reducing interpenetration. We introduce a novel
geometry-conditioned recurrent network with an encoder-space optimization
strategy that achieves efficient retargeting while satisfying contact
constraints. In experiments, our results quantitatively outperform previous
methods and we conduct a user study where our retargeted motions are rated as
higher-quality than those produced by recent works. We also show our method
generalizes to motion estimated from human videos where we improve over
previous works that produce noticeable interpenetration.

本文提出了一种运动重新定位方法，能够保留自身联系并防止相互穿透，利用一种新颖的几何条件循环网络和编码器空间优化策略实现高效的重新定位，通过实验证明其结果优于以往方法。