GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning
Authors: name: Ye Yuan, name: Xueting Li, name: Yangyi Huang, name: Shalini De Mello, name: Koki Nagano, name: Jan Kautz, name: Umar Iqbal

Gaussian splatting has emerged as a powerful 3D representation that harnesses
the advantages of both explicit (mesh) and implicit (NeRF) 3D representations.
In this paper, we seek to leverage Gaussian splatting to generate realistic
animatable avatars from textual descriptions, addressing the limitations (e.g.,
flexibility and efficiency) imposed by mesh or NeRF-based representations.
However, a naive application of Gaussian splatting cannot generate high-quality
animatable avatars and suffers from learning instability; it also cannot
capture fine avatar geometries and often leads to degenerate body parts. To
tackle these problems, we first propose a primitive-based 3D Gaussian
representation where Gaussians are defined inside pose-driven primitives to
facilitate animation. Second, to stabilize and amortize the learning of
millions of Gaussians, we propose to use neural implicit fields to predict the
Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries
and extract detailed meshes, we propose a novel SDF-based implicit mesh
learning approach for 3D Gaussians that regularizes the underlying geometries
and extracts highly detailed textured meshes. Our proposed method, GAvatar,
enables the large-scale generation of diverse animatable avatars using only
text prompts. GAvatar significantly surpasses existing methods in terms of both
appearance and geometry quality, and achieves extremely fast rendering (100
fps) at 1K resolution.