UniTEX：面向三维形状的通用高保真生成纹理技术

摘要

我们提出了UniTEX，一种新颖的两阶段3D纹理生成框架，旨在为3D资产创建高质量且一致的纹理。现有方法主要依赖于在将生成的多视角图像重新投影到3D形状后，通过基于UV的修复来细化纹理，这引入了与拓扑模糊性相关的挑战。为解决这一问题，我们提议绕过UV映射的限制，直接在统一的3D功能空间中操作。具体而言，我们首先提出通过纹理函数（TFs）将纹理生成提升至3D空间——这是一种连续的体积表示，仅基于表面接近度将任意3D点映射到纹理值，与网格拓扑无关。随后，我们提出使用基于Transformer的大规模纹理模型（LTM）直接从图像和几何输入预测这些TFs。为进一步提升纹理质量并利用强大的2D先验知识，我们开发了一种基于LoRA的高级策略，高效地适应大规模扩散Transformer（DiTs），用于高质量的多视角纹理合成，作为我们的第一阶段。大量实验表明，UniTEX在视觉质量和纹理完整性方面均优于现有方法，为自动化3D纹理生成提供了一个可推广且可扩展的解决方案。代码将发布于：https://github.com/YixunLiang/UniTEX。

English

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: https://github.com/YixunLiang/UniTEX.

UniTEX：面向三维形状的通用高保真生成纹理技术

UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes

摘要

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