  We present a framework for video modeling based on denoising diffusion
probabilistic models that produces long-duration video completions in a variety
of realistic environments. We introduce a generative model that can at
test-time sample any arbitrary subset of video frames conditioned on any other
subset and present an architecture adapted for this purpose. Doing so allows us
to efficiently compare and optimize a variety of schedules for the order in
which frames in a long video are sampled and use selective sparse and
long-range conditioning on previously sampled frames. We demonstrate improved
video modeling over prior work on a number of datasets and sample temporally
coherent videos over 25 minutes in length. We additionally release a new video
modeling dataset and semantically meaningful metrics based on videos generated
in the CARLA autonomous driving simulator.
