Reviews: Unsupervised Learning of View-invariant Action Representations

This paper addresses the problem of view-invariant action representation within an unsupervised learning framework. In particular, the unsupervised learning task is the prediction of 3D motion from different viewpoints. The proposed model comprises four modules: "encoder", "cross-view decoder", "reconstruction decoder" and "view classifier". For training purposes, a loss function defined as the linear combination of three task-specific losses is proposed. Given an encoding, the cross-view decoder is in charge of estimating the 3D flow in a target view different of the source one.