Appearance of dressed humans undergoes a complex geometric transformation
induced not only by the static pose but also by its dynamics, i.e., there
exists a number of cloth geometric configurations given a pose depending on the
way it has moved. Such appearance modeling conditioned on motion has been
largely neglected in existing human rendering methods, resulting in rendering
of physically implausible motion. A key challenge of learning the dynamics of
the appearance lies in the requirement of a prohibitively large amount of
observations. In this paper, we present a compact motion representation by
enforcing equivariance -- a representation is expected to be transformed in the
way that the pose is transformed. We model an equivariant encoder that can
generate the generalizable representation from the spatial and temporal
derivatives of the 3D body surface. This learned representation is decoded by a
compositional multi-task decoder that renders high fidelity time-varying
appearance. Our experiments show that our method can generate a temporally
coherent video of dynamic humans for unseen body poses and novel views given a
single view video.

本研究提出一种新的具有运动条件的人类动态外观模型，使用等变编码器从三维身体表面的空间和时间导数生成可推广的表征，再通过组合式多任务解码器渲染具有高保真度的时间变化外观的视频，实验结果表明，本方法能生成视角全新的人类动态视频。