  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.
