Texture Generation on 3D Meshes with Point-UV Diffusion
August 21, 2023 Β· Declared Dead Β· π IEEE International Conference on Computer Vision
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Authors
Xin Yu, Peng Dai, Wenbo Li, Lan Ma, Zhengzhe Liu, Xiaojuan Qi
arXiv ID
2308.10490
Category
cs.CV: Computer Vision
Cross-listed
cs.AI,
cs.GR
Citations
73
Venue
IEEE International Conference on Computer Vision
Last Checked
5 months ago
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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