  Generative models of natural images have progressed towards high fidelity
samples by the strong leveraging of scale. We attempt to carry this success to
the field of video modeling by showing that large Generative Adversarial
Networks trained on the complex Kinetics-600 dataset are able to produce video
samples of substantially higher complexity and fidelity than previous work. Our
proposed model, Dual Video Discriminator GAN (DVD-GAN), scales to longer and
higher resolution videos by leveraging a computationally efficient
decomposition of its discriminator. We evaluate on the related tasks of video
synthesis and video prediction, and achieve new state-of-the-art Fr\'echet
Inception Distance for prediction for Kinetics-600, as well as state-of-the-art
Inception Score for synthesis on the UCF-101 dataset, alongside establishing a
strong baseline for synthesis on Kinetics-600.
