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Despite tremendous progress in generating high-quality images using diffusionmodels, synthesizing a sequence of animated frames that are both photorealisticand temporally coherent is still in its infancy. While off-the-shelfbillion-scale datasets for image generation are available, collecting similarvideo data of the same scale is still challenging. Also, training a videodiffusion model is computationally much more expensive than its imagecounterpart. In this work, we explore finetuning a pretrained image diffusionmodel with video data as a practical solution for the video synthesis task. Wefind that naively extending the image noise prior to video noise prior in videodiffusion leads to sub-optimal performance. Our carefully designed video noiseprior leads to substantially better performance. Extensive experimentalvalidation shows that our model, Preserve Your Own Correlation (PYoCo), attainsSOTA zero-shot text-to-video results on the UCF-101 and MSR-VTT benchmarks. Italso achieves SOTA video generation quality on the small-scale UCF-101benchmark with a $10\times$ smaller model using significantly less computationthan the prior art.

Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models