  We propose a method for learning landmark detectors for visual objects (such
as the eyes and the nose in a face) without any manual supervision. We cast
this as the problem of generating images that combine the appearance of the
object as seen in a first example image with the geometry of the object as seen
in a second example image, where the two examples differ by a viewpoint change
and/or an object deformation. In order to factorize appearance and geometry, we
introduce a tight bottleneck in the geometry-extraction process that selects
and distils geometry-related features. Compared to standard image generation
problems, which often use generative adversarial networks, our generation task
is conditioned on both appearance and geometry and thus is significantly less
ambiguous, to the point that adopting a simple perceptual loss formulation is
sufficient. We demonstrate that our approach can learn object landmarks from
synthetic image deformations or videos, all without manual supervision, while
outperforming state-of-the-art unsupervised landmark detectors. We further show
that our method is applicable to a large variety of datasets - faces, people,
3D objects, and digits - without any modifications.
