UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes

• URL: http://arxiv.org/abs/2505.23253v1
• Date: Thu, 29 May 2025 08:58:41 GMT
• Title: UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes
• Authors: Yixun Liang, Kunming Luo, Xiao Chen, Rui Chen, Hongyu Yan, Weiyu Li, Jiarui Liu, Ping Tan,
• Abstract summary: We present UniTEX, a novel two-stage 3D texture generation framework.<n>UniTEX achieves superior visual quality and texture integrity compared to existing approaches.
• Score: 35.667175445637604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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: https://github.com/YixunLiang/UniTEX.

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