Paint3D: Paint Anything 3D With Lighting-Less Texture Diffusion Models

Xianfang Zeng, Xin Chen, Zhongqi Qi, Wen Liu, Zibo Zhao +4 more
2/14/2026

No official code found. Best implementation: OpenTexture/Paint3D (804 stars, Apache-2.0, last commit 2024-11-05).

Abstract

This paper presents Paint3D, a novel coarse-to-fine generative framework that is capable of producing high-resolution, lighting-less, and diverse 2K UV texture maps for untextured 3D meshes conditioned on text or image inputs. The key challenge addressed is generating high-quality textures without embedded illumination information, which allows the textures to be re-lighted or re-edited within modern graphics pipelines. To achieve this, our method first leverages a pre-trained depth-aware 2D diffusion model to generate view-conditional images and per-form multi-view texture fusion, producing an initial coarse texture map. However, as 2D models cannot fully repre-sent 3D shapes and disable lighting effects, the coarse texture map exhibits incomplete areas and illumination artifacts. To resolve this, we train separate UV Inpainting and UVHD diffusion models specialized for the shape-aware re-finement of incomplete areas and the removal of illumination artifacts. Through this coarse-to-fine process, Paint3D can produce high-quality 2K UV textures that maintain se-mantic consistency while being lighting-less, significantly advancing the state-of-the-art in texturing 3D objects.

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Code Implementations(1)

OpenTexture/Paint3DCommunity100%
Apache-2.0

[CVPR 2024] Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models, a no lighting baked texture generative model

80443PythonNov 30, 20231 years agoApache-2.0
diffusion-modelsgenerative-aigenerative-modelstable-diffusiontexture+1 more

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Cite this paper

@article{zeng2026paintd,
  title  = {Paint3D: Paint Anything 3D With Lighting-Less Texture Diffusion Models},
  author = {Xianfang Zeng and Xin Chen and Zhongqi Qi and Wen Liu and Zibo Zhao and Zhibin Wang and Bin Fu and Yong Liu and Gang Yu},
  year   = {2026},
  eprint = {2312.13913},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/pdf/2312.13913},
  journal = {CVPR 2023 2023}
}

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