PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual
  Observations
Authors: name: Yang Zheng; name: Qingqing Zhao; name: Guandao Yang; name: Wang Yifan; name: Donglai Xiang; name: Florian Dubost; name: Dmitry Lagun; name: Thabo Beeler; name: Federico Tombari; name: Leonidas Guibas; name: Gordon Wetzstein
Published: 2024-04-05 21:44:57

Abstract: Modeling and rendering photorealistic avatars is of crucial importance in
many applications. Existing methods that build a 3D avatar from visual
observations, however, struggle to reconstruct clothed humans. We introduce
PhysAvatar, a novel framework that combines inverse rendering with inverse
physics to automatically estimate the shape and appearance of a human from
multi-view video data along with the physical parameters of the fabric of their
clothes. For this purpose, we adopt a mesh-aligned 4D Gaussian technique for
spatio-temporal mesh tracking as well as a physically based inverse renderer to
estimate the intrinsic material properties. PhysAvatar integrates a physics
simulator to estimate the physical parameters of the garments using
gradient-based optimization in a principled manner. These novel capabilities
enable PhysAvatar to create high-quality novel-view renderings of avatars
dressed in loose-fitting clothes under motions and lighting conditions not seen
in the training data. This marks a significant advancement towards modeling
photorealistic digital humans using physically based inverse rendering with
physics in the loop. Our project website is at:
https://qingqing-zhao.github.io/PhysAvatar