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