TUVF: Learning Generalizable Texture UV Radiance Fields
Authors: name: An-Chieh Cheng, name: Xueting Li, name: Sifei Liu, name: Xiaolong Wang

Textures are a vital aspect of creating visually appealing and realistic 3D
models. In this paper, we study the problem of generating high-fidelity texture
given shapes of 3D assets, which has been relatively less explored compared
with generic 3D shape modeling. Our goal is to facilitate a controllable
texture generation process, such that one texture code can correspond to a
particular appearance style independent of any input shapes from a category. We
introduce Texture UV Radiance Fields (TUVF) that generate textures in a
learnable UV sphere space rather than directly on the 3D shape. This allows the
texture to be disentangled from the underlying shape and transferable to other
shapes that share the same UV space, i.e., from the same category. We integrate
the UV sphere space with the radiance field, which provides a more efficient
and accurate representation of textures than traditional texture maps. We
perform our experiments on real-world object datasets where we achieve not only
realistic synthesis but also substantial improvements over state-of-the-arts on
texture controlling and editing. Project Page: https://www.anjiecheng.me/TUVF