TriTex: Learning Texture from a Single Mesh via Triplane Semantic Features

• URL: http://arxiv.org/abs/2503.16630v1
• Date: Thu, 20 Mar 2025 18:35:03 GMT
• Title: TriTex: Learning Texture from a Single Mesh via Triplane Semantic Features
• Authors: Dana Cohen-Bar, Daniel Cohen-Or, Gal Chechik, Yoni Kasten,
• Abstract summary: We present a novel approach that learns a volumetric texture field from a single textured mesh by mapping semantic features to surface target colors.<n>Our approach achieves superior texture quality across 3D models in applications like game development.
• Score: 78.13246375582906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As 3D content creation continues to grow, transferring semantic textures between 3D meshes remains a significant challenge in computer graphics. While recent methods leverage text-to-image diffusion models for texturing, they often struggle to preserve the appearance of the source texture during texture transfer. We present \ourmethod, a novel approach that learns a volumetric texture field from a single textured mesh by mapping semantic features to surface colors. Using an efficient triplane-based architecture, our method enables semantic-aware texture transfer to a novel target mesh. Despite training on just one example, it generalizes effectively to diverse shapes within the same category. Extensive evaluation on our newly created benchmark dataset shows that \ourmethod{} achieves superior texture transfer quality and fast inference times compared to existing methods. Our approach advances single-example texture transfer, providing a practical solution for maintaining visual coherence across related 3D models in applications like game development and simulation.

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