  The video generation task can be formulated as a prediction of future video
frames given some past frames. Recent generative models for videos face the
problem of high computational requirements. Some models require up to 512
Tensor Processing Units for parallel training. In this work, we address this
problem via modeling the dynamics in a latent space. After the transformation
of frames into the latent space, our model predicts latent representation for
the next frames in an autoregressive manner. We demonstrate the performance of
our approach on BAIR Robot Pushing and Kinetics-600 datasets. The approach
tends to reduce requirements to 8 Graphical Processing Units for training the
models while maintaining comparable generation quality.
