Despite tremendous progress in generating high-quality images using diffusion models, synthesizing a sequence of animated frames that are both photorealistic and temporally coherent is still in its infancy. While off-the-shelf billion-scale datasets for image generation are available, collecting similar video data of the same scale is still challenging. Also, training a video diffusion model is computationally much more expensive than its image counterpart. In this work, we explore finetuning a pretrained image diffusion model with video data as a practical solution for the video synthesis task. We find that naively extending the image noise prior to video noise prior in video diffusion leads to sub-optimal performance. Our carefully designed video noise prior leads to substantially better performance. Extensive experimental validation shows that our model, Preserve Your Own Correlation (PYoCo), attains SOTA zero-shot text-to-video results on the UCF-101 and MSR-VTT benchmarks. It also achieves SOTA video generation quality on the small-scale UCF-101 benchmark with a 10× smaller model using significantly less computation than the prior art.
@article{arxiv.2305.10474,
title = {Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models},
author = {Songwei Ge and Seungjun Nah and Guilin Liu and Tyler Poon and Andrew Tao and Bryan Catanzaro and David Jacobs and Jia-Bin Huang and Ming-Yu Liu and Yogesh Balaji},
journal= {arXiv preprint arXiv:2305.10474},
year = {2024}
}