GETAvatar: Generative Textured Meshes for Animatable Human Avatars

Xuanmeng Zhang, Jianfeng Zhang, Rohan Chacko, Hongyi Xu, Guoxian Song, Yi Yang

Abstract:

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 photorealistic 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 rasterizationbased 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 5122
resolution with
17FPS and 10242
resolution with 14FPS, improving upon
previous methods by 2×.