  The task of video prediction is forecasting the next frames given some
previous frames. Despite much recent progress, this task is still challenging
mainly due to high nonlinearity in the spatial domain. To address this issue,
we propose a novel architecture, Frequency Domain Transformer Network (FDTN),
which is an end-to-end learnable model that estimates and uses the
transformations of the signal in the frequency domain. Experimental evaluations
show that this approach can outperform some widely used video prediction
methods like Video Ladder Network (VLN) and Predictive Gated Pyramids (PGP).
