$E^3$Gen: Efficient, Expressive and Editable Avatars Generation
<|reference_start|>$E^3$Gen: Efficient, Expressive and Editable Avatars Generation: This paper aims to introduce 3D Gaussian for efficient, expressive, and editable digital avatar generation. This task faces two major challenges: (1) The unstructured nature of 3D Gaussian makes it incompatible with current generation pipelines; (2) the expressive animation of 3D Gaussian 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 plane representation that encodes unstructured 3D Gaussian onto a structured 2D UV space defined by the SMPL-X parametric model. This novel representation not only preserves the representation ability of the original 3D Gaussian 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. Our project page is https://olivia23333.github.io/E3Gen.<|reference_end|>
@article{zhang2024$e^{3}$gen:,
    title={$E^{3}$Gen: Efficient, Expressive and Editable Avatars Generation},
    author={Weitian Zhang, Yichao Yan, Yunhui Liu, Xingdong Sheng, Xiaokang Yang},
    journal={arXiv preprint arXiv:2405.19203},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19203},
    primaryClass={cs.CV}
}