TEXGen: a Generative Diffusion Model for Mesh Textures
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/.
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Xin Yu
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2025-03-12T15:26:25.247Z
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Xin Yu
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Andyx
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Ze Yuan
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2024-11-26T21:20:58.452Z
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Ze Yuan
user
yuanze1024
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Yuan-Chen Guo
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Ying-Tian Liu
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JianHui Liu
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Yangguang Li
extracted_confirmed
2025-02-15T12:09:31.821Z
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Yangguang Li
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Lp256
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Yan-Pei Cao
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Ding Liang
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Xiaojuan Qi
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2025-07-15T19:14:26.464Z
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xiaojuan qi
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xjqi
TEXGen: a Generative Diffusion Model for Mesh Textures
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/.
A large diffusion model trained directly in UV texture space generates high-resolution texture maps from text prompts and images, supporting applications like inpainting, completion, and synthesis.
diffusion models UV texture space convolution attention layers point clouds text-guided texture inpainting sparse-view texture completion text-driven texture synthesis