DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks

@article{Su2022DANBODA,
  title={DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks},
  author={Shih-Yang Su and Timur M. Bagautdinov and Helge Rhodin},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.01666}
}
Deep learning greatly improved the realism of animatable human models by learning geometry and appearance from collections of 3D scans, template meshes, and multi-view imagery. High-resolution models enable photo-realistic avatars but at the cost of requiring studio settings not available to end users. Our goal is to create avatars directly from raw images without relying on expensive studio se-tups and surface tracking. While a few such approaches exist, those have limited generalization… 

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