Sayan Deb Sarkar

Fine-grained 3D object generation is now possible by directly controlling local geometry and appearance with text and shape primitives, bypassing the need for model retraining.

FAMOS, a feed-forward model that predicts movable-part segmentation and joint parameters from a sparse, unordered set of partial point clouds, is presented and a procedural data generator that synthesizes self-annotated assets during training is introduced to overcome the limited scale and diversity of existing datasets.