IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans
  from Monocular Videos via Explicit Ray Tracing
Shaofei Wang Božidar Antić Andreas Geiger Siyu Tang
We present IntrinsicAvatar, a novel approach to recovering the intrinsic
properties of clothed human avatars including geometry, albedo, material, and
environment lighting from only monocular videos. Recent advancements in
human-based neural rendering have enabled high-quality geometry and appearance
reconstruction of clothed humans from just monocular videos. However, these
methods bake intrinsic properties such as albedo, material, and environment
lighting into a single entangled neural representation. On the other hand, only
a handful of works tackle the problem of estimating geometry and disentangled
appearance properties of clothed humans from monocular videos. They usually
achieve limited quality and disentanglement due to approximations of secondary
shading effects via learned MLPs. In this work, we propose to model secondary
shading effects explicitly via Monte-Carlo ray tracing. We model the rendering
process of clothed humans as a volumetric scattering process, and combine ray
tracing with body articulation. Our approach can recover high-quality geometry,
albedo, material, and lighting properties of clothed humans from a single
monocular video, without requiring supervised pre-training using ground truth
materials. Furthermore, since we explicitly model the volumetric scattering
process and ray tracing, our model naturally generalizes to novel poses,
enabling animation of the reconstructed avatar in novel lighting conditions.