title: TEXGen: a Generative Diffusion Model for Mesh Textures
summary: While high-quality texture maps are essential for realistic 3D asset
  rendering, few studies have explored learning directly in the texture space,
  especially on large-scale datasets. In this work, we depart from the
  conventional approach of relying on pre-trained 2D diffusion models for
  test-time optimization of 3D textures. Instead, we focus on the fundamental
  problem of learning in the UV texture space itself. For the first time, we
  train a large diffusion model capable of directly generating high-resolution
  texture maps in a feed-forward manner. To facilitate efficient learning in
  high-resolution UV spaces, we propose a scalable network architecture that
  interleaves convolutions on UV maps with attention layers on point clouds.
  Leveraging this architectural design, we train a 700 million parameter
  diffusion model that can generate UV texture maps guided by text prompts and
  single-view images. Once trained, our model naturally supports various extended
  applications, including text-guided texture inpainting, sparse-view texture
  completion, and text-driven texture synthesis. Project page is at
  http://cvmi-lab.github.io/TEXGen/.
paper_authors._id: 674559365ac931cfc8367ddb
paper_authors.name: Xin Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-12T15:26:25.247Z
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paper_authors.avatarUrl: /avatars/6dbfba55563c71ed0db0d59c2629d525.svg
paper_authors.fullname: Xin Yu
paper_authors.type: user
paper_authors.user: Andyx
paper_authors._id: 674559365ac931cfc8367ddc
paper_authors.name: Ze Yuan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-26T21:20:58.452Z
paper_authors._id: 64ad15a05d48838462e46687
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paper_authors.fullname: Ze Yuan
paper_authors.type: user
paper_authors.user: yuanze1024
paper_authors._id: 674559365ac931cfc8367ddd
paper_authors.name: Yuan-Chen Guo
paper_authors._id: 674559365ac931cfc8367dde
paper_authors.name: Ying-Tian Liu
paper_authors._id: 674559365ac931cfc8367ddf
paper_authors.name: JianHui Liu
paper_authors._id: 674559365ac931cfc8367de0
paper_authors.name: Yangguang Li
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-15T12:09:31.821Z
paper_authors._id: 64d71083a787c9bc7b9f1238
paper_authors.avatarUrl: /avatars/d0b0546dec7fc5792921154bec41385a.svg
paper_authors.fullname: Yangguang Li
paper_authors.type: user
paper_authors.user: Lp256
paper_authors._id: 674559365ac931cfc8367de1
paper_authors.name: Yan-Pei Cao
paper_authors._id: 674559365ac931cfc8367de2
paper_authors.name: Ding Liang
paper_authors._id: 674559365ac931cfc8367de3
paper_authors.name: Xiaojuan Qi
paper_title: TEXGen: a Generative Diffusion Model for Mesh Textures
paper_summary: While high-quality texture maps are essential for realistic 3D asset
  rendering, few studies have explored learning directly in the texture space,
  especially on large-scale datasets. In this work, we depart from the
  conventional approach of relying on pre-trained 2D diffusion models for
  test-time optimization of 3D textures. Instead, we focus on the fundamental
  problem of learning in the UV texture space itself. For the first time, we
  train a large diffusion model capable of directly generating high-resolution
  texture maps in a feed-forward manner. To facilitate efficient learning in
  high-resolution UV spaces, we propose a scalable network architecture that
  interleaves convolutions on UV maps with attention layers on point clouds.
  Leveraging this architectural design, we train a 700 million parameter
  diffusion model that can generate UV texture maps guided by text prompts and
  single-view images. Once trained, our model naturally supports various extended
  applications, including text-guided texture inpainting, sparse-view texture
  completion, and text-driven texture synthesis. Project page is at
  http://cvmi-lab.github.io/TEXGen/.
paper_ai_keywords: diffusion model UV texture space pre-trained 2D diffusion models feed-forward manner scalable network architecture attention layers high-resolution texture maps text prompts text-guided texture inpainting sparse-view texture completion text-driven texture synthesis