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.
@article{arxiv.1803.07488,
title = {Dynamic Variational Autoencoders for Visual Process Modeling},
author = {Alexander Sagel and Hao Shen},
journal= {arXiv preprint arXiv:1803.07488},
year = {2020}
}