GETAvatar: Generative Textured Meshes for Animatable Human Avatars
Xuanmeng Zhang Jianfeng Zhang Rohan Chacko Hongyi Xu Guoxian Song Yi Yang Jiashi Feng
We study the problem of 3D-aware full-body human generation, aiming at
creating animatable human avatars with high-quality textures and geometries.
Generally, two challenges remain in this field: i) existing methods struggle to
generate geometries with rich realistic details such as the wrinkles of
garments; ii) they typically utilize volumetric radiance fields and neural
renderers in the synthesis process, making high-resolution rendering
non-trivial. To overcome these problems, we propose GETAvatar, a Generative
model that directly generates Explicit Textured 3D meshes for animatable human
Avatar, with photo-realistic appearance and fine geometric details.
Specifically, we first design an articulated 3D human representation with
explicit surface modeling, and enrich the generated humans with realistic
surface details by learning from the 2D normal maps of 3D scan data. Second,
with the explicit mesh representation, we can use a rasterization-based
renderer to perform surface rendering, allowing us to achieve high-resolution
image generation efficiently. Extensive experiments demonstrate that GETAvatar
achieves state-of-the-art performance on 3D-aware human generation both in
appearance and geometry quality. Notably, GETAvatar can generate images at
512x512 resolution with 17FPS and 1024x1024 resolution with 14FPS, improving
upon previous methods by 2x. Our code and models will be available.