Prompting Image Generators for Training-free Primitive Shape Abstraction
This work creates a training-free pipeline to turn 3D object renders into simple geometric shapes using generalist image models. It uses a vision-language model to guide a generative model in segmenting parts, which are then fitted with superquadric primitives. This method proves that part segmentation is the main hurdle in 3D shape abstraction.