UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes
Authors: name: Yixun Liang, name: Kunming Luo, name: Xiao Chen, name: Rui Chen, name: Hongyu Yan, name: Weiyu Li, name: Jiarui Liu, name: Ping Tan

We present UniTEX, a novel two-stage 3D texture generation framework to
create high-quality, consistent textures for 3D assets. Existing approaches
predominantly rely on UV-based inpainting to refine textures after reprojecting
the generated multi-view images onto the 3D shapes, which introduces challenges
related to topological ambiguity. To address this, we propose to bypass the
limitations of UV mapping by operating directly in a unified 3D functional
space. Specifically, we first propose that lifts texture generation into 3D
space via Texture Functions (TFs)--a continuous, volumetric representation that
maps any 3D point to a texture value based solely on surface proximity,
independent of mesh topology. Then, we propose to predict these TFs directly
from images and geometry inputs using a transformer-based Large Texturing Model
(LTM). To further enhance texture quality and leverage powerful 2D priors, we
develop an advanced LoRA-based strategy for efficiently adapting large-scale
Diffusion Transformers (DiTs) for high-quality multi-view texture synthesis as
our first stage. Extensive experiments demonstrate that UniTEX achieves
superior visual quality and texture integrity compared to existing approaches,
offering a generalizable and scalable solution for automated 3D texture
generation. Code will available in: https://github.com/YixunLiang/UniTEX.