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 $10\times$ smaller model using significantly less computation than the prior art.

翻译：尽管利用扩散模型生成高质量图像方面取得了巨大进展，但合成既具有真实感又具有时间一致性的动画帧序列仍处于起步阶段。虽然存在现成的数十亿级图像生成数据集，但收集同等规模的视频数据仍具挑战性。此外，训练视频扩散模型的计算成本远高于图像扩散模型。本研究探索将预训练图像扩散模型与视频数据微调作为视频合成任务的实用方案。我们发现，将图像噪声先验简单扩展到视频噪声先验会导致次优性能，而精心设计的视频噪声先验能显著提升效果。大量实验验证表明，我们的模型"保留自身相关性"（Preserve Your Own Correlation, PYoCo）在UCF-101和MSR-VTT基准测试中取得了最先进的零样本文本到视频生成结果。同时，在小型UCF-101基准测试中，该模型以比先前技术少10倍的模型规模和显著更少的计算量，实现了最先进的视频生成质量。