Generating and representing human behavior are of major importance for
various computer vision applications. Commonly, human video synthesis
represents behavior as sequences of postures while directly predicting their
likely progressions or merely changing the appearance of the depicted persons,
thus not being able to exercise control over their actual behavior during the
synthesis process. In contrast, controlled behavior synthesis and transfer
across individuals requires a deep understanding of body dynamics and calls for
a representation of behavior that is independent of appearance and also of
specific postures. In this work, we present a model for human behavior
synthesis which learns a dedicated representation of human dynamics independent
of postures. Using this representation, we are able to change the behavior of a
person depicted in an arbitrary posture, or to even directly transfer behavior
observed in a given video sequence. To this end, we propose a conditional
variational framework which explicitly disentangles posture from behavior. We
demonstrate the effectiveness of our approach on this novel task, evaluating
capturing, transferring, and sampling fine-grained, diverse behavior, both
quantitatively and qualitatively. Project page is available at
this https URL

该研究介绍了一种控制人类行为综合和转移的模型，该模型学习了一个独立于姿势的人体动态专用表示。使用这个表示，可以改变任何姿势下的一个人的行为，或者直接转移给定视频序列中观察到的行为。