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We introduce PartGlot, a neural framework and associated architectures forlearning semantic part segmentation of 3D shape geometry, based solely on partreferential language. We exploit the fact that linguistic descriptions of ashape can provide priors on the shape's parts -- as natural language hasevolved to reflect human perception of the compositional structure of objects,essential to their recognition and use. For training, we use the pairedgeometry / language data collected in the ShapeGlot work for their referencegame, where a speaker creates an utterance to differentiate a target shape fromtwo distractors and the listener has to find the target based on thisutterance. Our network is designed to solve this target discrimination problem,carefully incorporating a Transformer-based attention module so that the outputattention can precisely highlight the semantic part or parts described in thelanguage. Furthermore, the network operates without any direct supervision onthe 3D geometry itself. Surprisingly, we further demonstrate that the learnedpart information is generalizable to shape classes unseen during training. Ourapproach opens the possibility of learning 3D shape parts from language alone,without the need for large-scale part geometry annotations, thus facilitatingannotation acquisition.

PartGlot: Learning Shape Part Segmentation from Language Reference Games