The appearance of a human in clothing is driven not only by the pose but also
by its temporal context, i.e., motion. However, such context has been largely
neglected by existing monocular human modeling methods whose neural networks
often struggle to learn a video of a person with large dynamics due to the
motion ambiguity, i.e., there exist numerous geometric configurations of
clothes that are dependent on the context of motion even for the same pose. In
this paper, we introduce a method for high-quality modeling of clothed 3D human
avatars using a video of a person with dynamic movements. The main challenge
comes from the lack of 3D ground truth data of geometry and its temporal
correspondences. We address this challenge by introducing a novel compositional
human modeling framework that takes advantage of both explicit and implicit
human modeling. For explicit modeling, a neural network learns to generate
point-wise shape residuals and appearance features of a 3D body model by
comparing its 2D rendering results and the original images. This explicit model
allows for the reconstruction of discriminative 3D motion features from UV
space by encoding their temporal correspondences. For implicit modeling, an
implicit network combines the appearance and 3D motion features to decode
high-fidelity clothed 3D human avatars with motion-dependent geometry and
texture. The experiments show that our method can generate a large variation of
secondary motion in a physically plausible way.

使用一个人的动态视频，通过引入新颖的组合式人体建模框架，结合显式建模和隐式建模的方法，实现高质量的服装 3D 人体化身建模，解决了缺乏几何和其时间对应关系的 3D 真值数据的挑战，成功地生成了具有运动相关几何和纹理的服装 3D 人体化身。