@inproceedings{
zhang2024egen,
title={\$E{\textasciicircum}\{3\}\$Gen: Efficient, Expressive and Editable Avatars Generation},
author={Weitian Zhang and Yichao Yan and Yunhui Liu and Xingdong Sheng and Xiaokang Yang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=WDAsva0ddw}
}
This paper aims to introduce 3D Gaussians for efficient, expressive, and editable digital avatar generation.This task faces two major challenges: 1) The unstructured nature of 3D Gaussians makes it incompatible with current generation pipelines; 2) the animation of 3D Gaussians in a generative setting that involves training with multiple subjects remains unexplored. In this paper, we propose a novel avatar generation method named $E^{3}$Gen, to effectively address these challenges. First, we propose a novel generative UV features representation that encodes unstructured 3D Gaussians onto a structured 2D UV space defined by the SMPLX parametric model. This novel representation not only preserves the representation ability of the original 3D Gaussians but also introduces a shared structure among subjects to enable generative learning of the diffusion model. To tackle the second challenge, we propose a part-aware deformation module to achieve robust and accurate full-body expressive pose control. Extensive experiments demonstrate that our method achieves superior performance in avatar generation and enables expressive full-body pose control and editing.
E^3Gen: Efficient, Expressive and Editable Avatars Generation
Weitian Zhang Yichao Yan Yunhui Liu Xingdong Sheng Xiaokang Yang