  Human pose analysis is presently dominated by deep convolutional networks
trained with extensive manual annotations of joint locations and beyond. To
avoid the need for expensive labeling, we exploit spatiotemporal relations in
training videos for self-supervised learning of pose embeddings. The key idea
is to combine temporal ordering and spatial placement estimation as auxiliary
tasks for learning pose similarities in a Siamese convolutional network. Since
the self-supervised sampling of both tasks from natural videos can result in
ambiguous and incorrect training labels, our method employs a curriculum
learning idea that starts training with the most reliable data samples and
gradually increases the difficulty. To further refine the training process we
mine repetitive poses in individual videos which provide reliable labels while
removing inconsistencies. Our pose embeddings capture visual characteristics of
human pose that can boost existing supervised representations in human pose
estimation and retrieval. We report quantitative and qualitative results on
these tasks in Olympic Sports, Leeds Pose Sports and MPII Human Pose datasets.
