Texture Generation on 3D Meshes with Point-UV Diffusion

Xin Yu, Peng Dai, Wenbo Li, Lan Ma, Zhengzhe Liu +1 more
2/14/2026

No official code found. Best implementation: CVMI-Lab/Point-UV-Diffusion (219 stars, last commit 2023-12-06).

Abstract

In this work, we focus on synthesizing high-quality textures on 3D meshes. We present Point-UV diffusion, a coarse-to-fine pipeline that marries the denoising diffusion model with UV mapping to generate 3D consistent and high-quality texture images in UV space. We start with introducing a point diffusion model to synthesize low-frequency texture components with our tailored style guidance to tackle the biased color distribution. The derived coarse texture offers global consistency and serves as a condition for the subsequent UV diffusion stage, aiding in regularizing the model to generate a 3D consistent UV texture image. Then, a UV diffusion model with hybrid conditions is developed to enhance the texture fidelity in the 2D UV space. Our method can process meshes of any genus, generating diversified, geometry-compatible, and high-fidelity textures. Code is available at https://cvmi-lab.github.io/Point-UV-Diffusion.

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Code Implementations(1)

(ICCV2023) This is the official PyTorch implementation of ICCV2023 paper: Texture Generation on 3D Meshes with Point-UV Diffusion

21913C, C++Aug 14, 20232 years ago

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Cite this paper

@article{yu2026texture,
  title  = {Texture Generation on 3D Meshes with Point-UV Diffusion},
  author = {Xin Yu and Peng Dai and Wenbo Li and Lan Ma and Zhengzhe Liu and Xiaojuan Qi},
  year   = {2026},
  eprint = {2308.10490},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2308.10490},
  journal = {ICCV 2023 2023}
}

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