UniTEX：面向三维形状的通用高保真生成式纹理
UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes
用统一的 3D 函数空间直接回归纹理，绕开 UV 映射的拓扑歧义；把表面纹理扩展为连续体积场（Texture Functions）作为训练目标，这一思路可迁移到任何需要稠密 3D 场监督的重建/生成任务。
原文依据 bypass the limitations of UV mapping by introducing a Large Texturing Model (LTM) that directly regresses textures in a unified 3D functional space
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出 UniTEX，一种新颖的两阶段三维纹理生成框架，用于为三维资产生成高质量且一致的纹理。现有方法在第二阶段主要依赖基于 UV 的模型，在将生成的多视图图像重投影到三维形状之后对纹理进行细化，这带来了与拓扑歧义相关的挑战。为解决该问题，我们引入大型纹理模型（Large Texturing Model, LTM），在统一的三维函数空间中直接回归纹理，从而绕开 UV 映射的局限。此外，为对 LTM 实现更有效且更完整的监督，我们提出将定义在表面上的纹理扩展为连续的体积场，作为更优的训练目标，我们将其称为纹理函数（Texture Functions, TF）。最后，我们设计了一种基于 LoRA 的改进策略，用于高效地适配大规模二维扩散 Transformer（DiT），以作为第一阶段实现高质量的多视图纹理合成。大量实验表明，与现有方法相比，UniTEX 在视觉质量与纹理完整性上均取得更优表现，为自动化三维纹理生成提供了一种可泛化且可扩展的解决方案。代码见：https://github.com/HKUST-SAIL/UniTEX
英文原文摘要 1229 字符
We present UniTEX, a novel two-stage 3D texture generation framework to create high-quality, consistent textures for 3D assets. Existing approaches predominantly rely on UV-based models in the second stage to refine textures after reprojecting the generated multi-view images onto the 3D shapes, which introduces challenges related to topological ambiguity. To address this, we bypass the limitations of UV mapping by introducing a Large Texturing Model (LTM) that directly regresses textures in a unified 3D functional space. Moreover, to enable more effective and complete supervision of LTM, we propose to extend surface-defined textures into a continuous volumetric field to serve as an advanced training objective, which we refer to as Texture Functions (TF). Finally, we develop an advanced LoRA-based strategy for efficiently adapting large-scale 2D Diffusion Transformers (DiTs) for high-quality multi-view texture synthesis as our first stage. Extensive experiments demonstrate that UniTEX achieves superior visual quality and texture integrity compared to existing approaches, offering a generalizable and scalable solution for automated 3D texture generation. Code is available in: https://github.com/HKUST-SAIL/UniTEX