  Segmenting an image into its parts is a frequent preprocess for high-level
vision tasks such as image editing. However, annotating masks for supervised
training is expensive. Weakly-supervised and unsupervised methods exist, but
they depend on the comparison of pairs of images, such as from multi-views,
frames of videos, and image augmentation, which limits their applicability. To
address this, we propose a GAN-based approach that generates images conditioned
on latent masks, thereby alleviating full or weak annotations required in
previous approaches. We show that such mask-conditioned image generation can be
learned faithfully when conditioning the masks in a hierarchical manner on
latent keypoints that define the position of parts explicitly. Without
requiring supervision of masks or points, this strategy increases robustness to
viewpoint and object positions changes. It also lets us generate image-mask
pairs for training a segmentation network, which outperforms the
state-of-the-art unsupervised segmentation methods on established benchmarks.
