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This paper takes a step towards temporal reasoning in a dynamically changingvideo, not in the pixel space that constitutes its frames, but in a latentspace that describes the non-linear dynamics of the objects in its world. Weintroduce the Kalman variational auto-encoder, a framework for unsupervisedlearning of sequential data that disentangles two latent representations: anobject's representation, coming from a recognition model, and a latent statedescribing its dynamics. As a result, the evolution of the world can beimagined and missing data imputed, both without the need to generate highdimensional frames at each time step. The model is trained end-to-end on videosof a variety of simulated physical systems, and outperforms competing methodsin generative and missing data imputation tasks.

A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning