  Video generation necessitates both global coherence and local realism. This
work presents a novel non-autoregressive method GLOBER, which first generates
global features to obtain comprehensive global guidance and then synthesizes
video frames based on the global features to generate coherent videos.
Specifically, we propose a video auto-encoder, where a video encoder encodes
videos into global features, and a video decoder, built on a diffusion model,
decodes the global features and synthesizes video frames in a
non-autoregressive manner. To achieve maximum flexibility, our video decoder
perceives temporal information through normalized frame indexes, which enables
it to synthesize arbitrary sub video clips with predetermined starting and
ending frame indexes. Moreover, a novel adversarial loss is introduced to
improve the global coherence and local realism between the synthesized video
frames. Finally, we employ a diffusion-based video generator to fit the global
features outputted by the video encoder for video generation. Extensive
experimental results demonstrate the effectiveness and efficiency of our
proposed method, and new state-of-the-art results have been achieved on
multiple benchmarks.
