  Our goal is to predict future video frames given a sequence of input frames.
Despite large amounts of video data, this remains a challenging task because of
the high-dimensionality of video frames. We address this challenge by proposing
the Decompositional Disentangled Predictive Auto-Encoder (DDPAE), a framework
that combines structured probabilistic models and deep networks to
automatically (i) decompose the high-dimensional video that we aim to predict
into components, and (ii) disentangle each component to have low-dimensional
temporal dynamics that are easier to predict. Crucially, with an appropriately
specified generative model of video frames, our DDPAE is able to learn both the
latent decomposition and disentanglement without explicit supervision. For the
Moving MNIST dataset, we show that DDPAE is able to recover the underlying
components (individual digits) and disentanglement (appearance and location) as
we would intuitively do. We further demonstrate that DDPAE can be applied to
the Bouncing Balls dataset involving complex interactions between multiple
objects to predict the video frame directly from the pixels and recover
physical states without explicit supervision.
