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Video generation necessitates both global coherence and local realism. Thiswork presents a novel non-autoregressive method GLOBER, which first generatesglobal features to obtain comprehensive global guidance and then synthesizesvideo frames based on the global features to generate coherent videos.Specifically, we propose a video auto-encoder, where a video encoder encodesvideos into global features, and a video decoder, built on a diffusion model,decodes the global features and synthesizes video frames in anon-autoregressive manner. To achieve maximum flexibility, our video decoderperceives temporal information through normalized frame indexes, which enablesit to synthesize arbitrary sub video clips with predetermined starting andending frame indexes. Moreover, a novel adversarial loss is introduced toimprove the global coherence and local realism between the synthesized videoframes. Finally, we employ a diffusion-based video generator to fit the globalfeatures outputted by the video encoder for video generation. Extensiveexperimental results demonstrate the effectiveness and efficiency of ourproposed method, and new state-of-the-art results have been achieved onmultiple benchmarks.

GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER