In this work we develop 3D Paintbrush, a technique for automatically texturing local semantic regions on meshes via text descriptions. Our method is designed to operate directly on meshes, producing texture maps which seamlessly integrate into standard graphics pipelines. We opt to simultaneously produce a localization map (to specify the edit region) and a texture map which conforms to it. This synergistic approach improves the quality of both the localization and the stylization. To enhance the details and resolution of the textured area, we leverage multiple stages of a cascaded diffusion model to supervise our local editing technique with generative priors learned from images at different resolutions. Our technique, referred to as Cascaded Score Distillation (CSD), simultaneously distills scores at multiple resolutions in a cascaded fashion, enabling control over both the granularity and global understanding of the supervision. We demonstrate the effectiveness of 3D Paintbrush to locally texture a variety of shapes within different semantic regions. Project page: https://threedle.github.io/3d-paintbrush

翻译：本文提出了3D Paintbrush技术，通过文本描述自动为网格表面的局部语义区域生成纹理。该方法可直接作用于网格模型，生成与标准图形渲染管线无缝集成的纹理贴图。我们采用同步生成定位图（指定编辑区域）与对应纹理图的策略，这种协同优化方式同时提升了区域定位和风格化的质量。为增强纹理区域的细节与分辨率，我们利用级联扩散模型的多阶段架构进行监督，通过不同分辨率图像习得的生成先验指导局部编辑。所提出的级联分数蒸馏（CSD）技术以级联方式同时蒸馏多分辨率分数，实现了对监督信号粒度与全局理解的精细控制。实验证明，3D Paintbrush可有效对多种形状的不同语义区域进行局部纹理化。项目主页：https://threedle.github.io/3d-paintbrush