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.

本研究提出了一种基于去噪扩散概率模型的视频建模框架，可在各种真实环境下生成长时间视频，并介绍了一种适用于此目的的架构，可有效比较和优化采样顺序，并对以前采样的帧使用选择性稀疏和长程调节。实验结果表明，在多个数据集上相较于现有工作获得了更好的视频建模，并生成了长达 25 分钟的时间连贯视频。研究还发布了一个基于 CARLA 自动驾驶模拟器生成视频构建数据集和具有语义意义的度量。