  We address the problem of discovering part segmentations of articulated
objects without supervision. In contrast to keypoints, part segmentations
provide information about part localizations on the level of individual pixels.
Capturing both locations and semantics, they are an attractive target for
supervised learning approaches. However, large annotation costs limit the
scalability of supervised algorithms to other object categories than humans.
Unsupervised approaches potentially allow to use much more data at a lower
cost. Most existing unsupervised approaches focus on learning abstract
representations to be refined with supervision into the final representation.
Our approach leverages a generative model consisting of two disentangled
representations for an object's shape and appearance and a latent variable for
the part segmentation. From a single image, the trained model infers a semantic
part segmentation map. In experiments, we compare our approach to previous
state-of-the-art approaches and observe significant gains in segmentation
accuracy and shape consistency. Our work demonstrates the feasibility to
discover semantic part segmentations without supervision.
