Researchers propose FAMOS, a feed-forward model that predicts movable-part segmentation and joint parameters from sparse, unordered point clouds. The model jointly reasons over multiple observations and introduces a Multi-state Articulation Transformer for aggregating articulation cues. The authors also introduce a procedural data generator for synthesizing self-annotated assets during training.