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The paper introduces TexSpot, a diffusion-based texture enhancement framework that addresses view-inconsistency and resolution limitations in 3D texture generation. TexSpot utilizes a novel 3D texture representation called Texlet, which combines point-based and UV-based approaches by encoding local texture patches with a 2D encoder and aggregating them with a 3D encoder. Experiments show that TexSpot significantly improves visual fidelity, geometric consistency, and robustness compared to existing state-of-the-art methods.
Ditch UV maps and point clouds: TexSpot's new "Texlet" representation lets you diffuse directly on 3D textures with better fidelity and consistency.
High-quality 3D texture generation remains a fundamental challenge due to the view-inconsistency inherent in current mainstream multi-view diffusion pipelines. Existing representations either rely on UV maps, which suffer from distortion during unwrapping, or point-based methods, which tightly couple texture fidelity to geometric density that limits high-resolution texture generation. To address these limitations, we introduce TexSpot, a diffusion-based texture enhancement framework. At its core is Texlet, a novel 3D texture representation that merges the geometric expressiveness of point-based 3D textures with the compactness of UV-based representation. Each Texlet latent vector encodes a local texture patch via a 2D encoder and is further aggregated using a 3D encoder to incorporate global shape context. A cascaded 3D-to-2D decoder reconstructs high-quality texture patches, enabling the Texlet space learning. Leveraging this representation, we train a diffusion transformer conditioned on Texlets to refine and enhance textures produced by multi-view diffusion methods. Extensive experiments demonstrate that TexSpot significantly improves visual fidelity, geometric consistency, and robustness over existing state-of-the-art 3D texture generation and enhancement approaches. Project page: https://anonymous.4open.science/w/TexSpot-page-2D91.