Modeling Parts, Structure, and System Dynamics via Predictive Learning

Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future. In this paper, we propose a novel formulation that simultaneously learns a hierarchical, disentangled object representation and a dynamics model for object parts from unlabeled videos in a self-supervised manner... (read more)

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