仅用一个带纹理的3D模型，就能高效迁移到新模型上保持视觉一致。
TriTex: Learning Texture from a Single Mesh via Triplane Semantic Features
- 通过三角平面架构将语义特征映射到表面颜色，学习体积纹理场。
- 仅需单例训练，在同类别不同形状间实现高质量纹理迁移。
- 适用于游戏开发、仿真等需快速生成一致纹理的场景。
随着3D内容创作的发展，跨3D网格传递语义纹理仍是计算机图形学中的重大挑战。尽管近期方法利用文本到图像扩散模型进行纹理生成，但常难以保留源纹理外观。本文提出一种新方法，通过将语义特征映射至表面颜色，从单个带纹理网格中学习体积纹理场。采用高效的三平面架构，该方法可实现语义感知的纹理迁移至新目标网格。尽管仅在单个示例上训练，仍能有效泛化至同一类别内的多样化形状。在新构建的基准数据集上的大量评估表明，该方法在纹理迁移质量与推理速度方面均优于现有方法。本方案推进了单例纹理迁移技术，为游戏开发与仿真等应用中保持相关3D模型视觉一致性提供了实用解决方案。
原文摘要 · Abstract (English)
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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