3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score
  Distillation
Dale Decatur Itai Lang Kfir Aberman Rana Hanocka
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