English

Texture Generation on 3D Meshes with Point-UV Diffusion

Computer Vision and Pattern Recognition 2023-08-22 v1 Artificial Intelligence Graphics

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

Keywords

Cite

@article{arxiv.2308.10490,
  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},
  journal= {arXiv preprint arXiv:2308.10490},
  year   = {2023}
}

Comments

Accepted to ICCV 2023, Oral

R2 v1 2026-06-28T12:00:06.902Z