  The ability to anticipate the future is essential when making real time
critical decisions, provides valuable information to understand dynamic natural
scenes, and can help unsupervised video representation learning. State-of-art
video prediction is based on LSTM recursive networks and/or generative
adversarial network learning. These are complex architectures that need to
learn large numbers of parameters, are potentially hard to train, slow to run,
and may produce blurry predictions. In this paper, we introduce DYAN, a novel
network with very few parameters and easy to train, which produces accurate,
high quality frame predictions, significantly faster than previous approaches.
DYAN owes its good qualities to its encoder and decoder, which are designed
following concepts from systems identification theory and exploit the
dynamics-based invariants of the data. Extensive experiments using several
standard video datasets show that DYAN is superior generating frames and that
it generalizes well across domains.
