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Human actions are comprised of a sequence of poses. This makes videos ofhumans a rich and dense source of human poses. We propose an unsupervisedmethod to learn pose features from videos that exploits a signal which iscomplementary to appearance and can be used as supervision: motion. The keyidea is that humans go through poses in a predictable manner while performingactions. Hence, given two poses, it should be possible to model the motion thatcaused the change between them. We represent each of the poses as a feature ina CNN (Appearance ConvNet) and generate a motion encoding from optical flowmaps using a separate CNN (Motion ConvNet). The data for this task isautomatically generated allowing us to train without human supervision. Wedemonstrate the strength of the learned representation by finetuning thetrained model for Pose Estimation on the FLIC dataset, for static image actionrecognition on PASCAL and for action recognition in videos on UCF101 andHMDB51.

Pose from Action: Unsupervised Learning of Pose Features based on Motion