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.

翻译：高斯溅射作为一种强大的3D表示方法，融合了显式（网格）和隐式（NeRF）3D表示的优点。本文旨在利用高斯溅射从文本描述生成逼真的可动画化身，以解决基于网格或NeRF表示方法存在的局限性（如灵活性和效率）。然而，直接应用高斯溅射无法生成高质量的可动画化身，且存在学习不稳定性问题；此外，它也无法捕捉精致的化身几何结构，常导致身体部位退化。为解决这些问题，我们首先提出一种基于基元的3D高斯表示，将高斯定义在姿态驱动的基元内以促进动画生成。其次，为稳定并分摊数百万个高斯的学习过程，我们提出使用神经隐式场预测高斯属性（如颜色）。最后，为捕捉精细的化身几何结构并提取细节网格，我们针对3D高斯提出一种新颖的基于SDF的隐式网格学习方法，用于约束底层几何并提取高细节纹理网格。所提出的GAvatar方法仅需文本提示即可实现大规模多样化可动画化身的生成。GAvatar在外观和几何质量上显著超越现有方法，并能在1K分辨率下实现极快渲染（100 fps）。