Abstract
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 inpainting 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 propose to bypass the limitations of UV mapping by operating directly in a unified 3D functional space. Specifically, we first propose that lifts texture generation into 3D space via Texture Functions (TFs)--a continuous, volumetric representation that maps any 3D point to a texture value based solely on surface proximity, independent of mesh topology. Then, we propose to predict these TFs directly from images and geometry inputs using a transformer-based Large Texturing Model (LTM). To further enhance texture quality and leverage powerful 2D priors, we develop an advanced LoRA-based strategy for efficiently adapting large-scale 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 will available in: this https URL.
Abstract (translated)
我们介绍了UniTEX，这是一种新颖的两阶段3D纹理生成框架，用于为3D资产创建高质量且一致的纹理。现有方法主要依赖于基于UV的修复技术，在将生成的多视角图像重新投影到3D形状后进行纹理细化，这引入了与拓扑歧义相关的挑战。为了克服这一限制，我们提出直接在统一的3D功能空间中操作，绕过UV映射的局限性。具体而言，我们首先提出了通过Texture Functions (TFs) 将纹理生成提升到三维空间的方法——这是一种连续的体素表示形式，它根据表面距离将任何3D点映射到一个纹理值，与网格拓扑无关。 然后，我们提出使用基于变压器的大规模纹理模型（LTM）直接从图像和几何输入预测这些TFs。为了进一步提高纹理质量并利用强大的2D先验知识，我们开发了一种先进的LoRA策略，可以有效地调整大规模Diffusion Transformers (DiTs) 以进行高质量的多视角纹理合成作为我们的第一阶段。 广泛的实验表明，UniTEX在视觉质量和纹理完整性方面优于现有的方法，为自动化的3D纹理生成提供了通用且可扩展的解决方案。代码将在此 https URL 上提供。
URL
https://arxiv.org/abs/2505.23253
