This work studies the problem of modeling visual processes by leveraging deep
generative architectures for learning linear, Gaussian representations from
observed sequences. We propose a joint learning framework, combining a vector
autoregressive model and Variational Autoencoders. This results in an
architecture that allows Variational Autoencoders to simultaneously learn a
non-linear observation as well as a linear state model from sequences of
frames. We validate our approach on artificial sequences and dynamic textures