Prompting Image Generators for Training-free Primitive Shape Abstraction
Compact primitive abstractions represent 3D shapes with a few geometric primitives while preserving recognizable components. Learned methods depend on their training classes, and optimization-based methods split shapes geometrically rather than into parts. We instead reuse the visual part knowledge of pretrained models without task-specific training or fine-tuning. A vision-language model names parts in multi-view renders, and an unmodified image generator paints color-coded part masks. Reprojection and spatial clustering recover 3D instances, and a classical optimizer fits one tapered and bent superquadric per part. With five to eight primitives per object, the abstractions match the Cha...
| Authors | Gregor Kobsik, Tim Elsner, Leif Kobbelt |
|---|---|
| Primary category | cs.CV |
| Published | 6 July 2026 |
| Last updated at source | 30 September 2026 |
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