  We present a deep generative model that learns disentangled static and
dynamic representations of data from unordered input. Our approach exploits
regularities in sequential data that exist regardless of the order in which the
data is viewed. The result of our factorized graphical model is a
well-organized and coherent latent space for data dynamics. We demonstrate our
method on several synthetic dynamic datasets and real video data featuring
various facial expressions and head poses.
