  Videos can be created by first outlining a global view of the scene and then
adding local details. Inspired by this idea we propose a cascaded model for
video generation which follows a coarse to fine approach. First our model
generates a low resolution video, establishing the global scene structure,
which is then refined by subsequent cascade levels operating at larger
resolutions. We train each cascade level sequentially on partial views of the
videos, which reduces the computational complexity of our model and makes it
scalable to high-resolution videos with many frames. We empirically validate
our approach on UCF101 and Kinetics-600, for which our model is competitive
with the state-of-the-art. We further demonstrate the scaling capabilities of
our model and train a three-level model on the BDD100K dataset which generates
256x256 pixels videos with 48 frames.
