  To address the ill-posed problem caused by partial observations in monocular
human volumetric capture, we present AvatarCap, a novel framework that
introduces animatable avatars into the capture pipeline for high-fidelity
reconstruction in both visible and invisible regions. Our method firstly
creates an animatable avatar for the subject from a small number (~20) of 3D
scans as a prior. Then given a monocular RGB video of this subject, our method
integrates information from both the image observation and the avatar prior,
and accordingly recon-structs high-fidelity 3D textured models with dynamic
details regardless of the visibility. To learn an effective avatar for
volumetric capture from only few samples, we propose GeoTexAvatar, which
leverages both geometry and texture supervisions to constrain the
pose-dependent dynamics in a decomposed implicit manner. An avatar-conditioned
volumetric capture method that involves a canonical normal fusion and a
reconstruction network is further proposed to integrate both image observations
and avatar dynamics for high-fidelity reconstruction in both observed and
invisible regions. Overall, our method enables monocular human volumetric
capture with detailed and pose-dependent dynamics, and the experiments show
that our method outperforms state of the art. Code is available at
https://github.com/lizhe00/AvatarCap.
