用新型点隐空间表示，提升3D纹理生成的视觉质量与视角一致性。
TexSpot: 3D Texture Enhancement with Spatially-uniform Point Latent Representation
- 提出Texlet点隐空间表示，结合点云与UV图优点，减少展开失真。
- 通过级联3D到2D解码器重建高分辨率纹理，实现纹理细节增强。
- 适合需要高质量3D纹理的生成与修复任务，如游戏、影视建模。
高质量3D纹理生成仍面临视图不一致的挑战，现有方法或依赖易失真的UV映射，或采用点基表示，导致纹理保真度受几何密度限制。为此，本文提出基于扩散模型的纹理增强框架TexSpot。核心是新型3D纹理表示Texlet，通过2D编码器提取局部纹理块，再经3D编码器融合全局形状上下文。级联3D到2D解码器重建高质量纹理块，实现Texlet空间学习。在此基础上，训练一个以Texlet为条件的扩散变换器，用于优化多视角扩散生成的纹理。大量实验表明，TexSpot在视觉保真度、几何一致性及鲁棒性上均显著优于当前最先进方法。
原文摘要 · Abstract (English)
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://texlet-arch.github.io/TexSpot-page.
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