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arXiv · 2404.04421

PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations

Yang Zheng·Qingqing Zhao·Guandao Yang·Wang Yifan·Donglai Xiang·Florian Dubost·Dmitry Lagun·Thabo Beeler·Federico Tombari·Leonidas Guibas·Gordon Wetzstein
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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

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Yang Zheng, Qingqing Zhao, Guandao Yang, Wang Yifan, Donglai Xiang, Florian Dubost, Dmitry Lagun, Thabo Beeler, Federico Tombari, Leonidas Guibas, Gordon Wetzstein. 2024-04-05. PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations. https://arxiv.org/abs/2404.04421

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