Rigging and skinning clothed human avatars is a challenging task and
traditionally requires a lot of manual work and expertise. Recent methods
addressing it either generalize across different characters or focus on
capturing the dynamics of a single character observed under different pose
configurations. However, the former methods typically predict solely static
skinning weights, which perform poorly for highly articulated poses, and the
latter ones either require dense 3D character scans in different poses or
cannot generate an explicit mesh with vertex correspondence over time. To
address these challenges, we propose a fully automated approach for creating a
fully rigged character with pose-dependent skinning weights, which can be
solely learned from multi-view video. Therefore, we first acquire a rigged
template, which is then statically skinned. Next, a coordinate-based MLP learns
a skinning weights field parameterized over the position in a canonical pose
space and the respective pose. Moreover, we introduce our pose- and
view-dependent appearance field allowing us to differentiably render and
supervise the posed mesh using multi-view imagery. We show that our approach
outperforms state-of-the-art while not relying on dense 4D scans.

本文提出一种基于多视角视频学习的机器学习方法，自动创建可全姿态绑定的角色并生成基于姿态的绑定权重。