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

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arxiv 2312.13913 v2 pith:ZTKANTPH submitted 2023-12-21 cs.CV

classification cs.CV
keywords texturediffusionilluminationlighting-lessmodelspaint3dtexturesareas
verification ladder T0 review T1 audit T2 compute T3 formal
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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 perform multi-view texture fusion, producing an initial coarse texture map. However, as 2D models cannot fully represent 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 refinement 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 semantic consistency while being lighting-less, significantly advancing the state-of-the-art in texturing 3D objects.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models

    cs.GR 2025-06 conditional novelty 6.0 of 10

    A video-diffusion pipeline conditioned on geometry maps, followed by component-wise UV inpainting, produces more coherent and seam-free textures for 3D meshes than Text2Tex, Paint3D, and Meshy in the reported tests.

  2. Bridging Rendering and Generative Modeling with Monte Carlo Transport Scheduling

    cs.CV 2026-02 reject novelty 5.0 of 10

    A common variance-time SDE aligns Monte Carlo rendering noise with diffusion-model denoising, enabling low-spp render refinement and stage-ordered material control.

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