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

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.

@article{cohen-bar2025_2503.16630,
  title={ TriTex: Learning Texture from a Single Mesh via Triplane Semantic Features },
  author={ Dana Cohen-Bar and Daniel Cohen-Or and Gal Chechik and Yoni Kasten },
  journal={arXiv preprint arXiv:2503.16630},
  year={ 2025 }
}