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

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