  This work explores how to use self-supervised learning on videos to learn a
class-specific image embedding that encodes pose and shape information. At
train time, two frames of the same video of an object class (e.g. human upper
body) are extracted and each encoded to an embedding. Conditioned on these
embeddings, the decoder network is tasked to transform one frame into another.
To successfully perform long range transformations (e.g. a wrist lowered in one
image should be mapped to the same wrist raised in another), we introduce a
hierarchical probabilistic network decoder model. Once trained, the embedding
can be used for a variety of downstream tasks and domains. We demonstrate our
approach quantitatively on three distinct deformable object classes -- human
full bodies, upper bodies, faces -- and show experimentally that the learned
embeddings do indeed generalise. They achieve state-of-the-art performance in
comparison to other self-supervised methods trained on the same datasets, and
approach the performance of fully supervised methods.
