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：尽管使用扩散模型在生成高质量图像方面取得了巨大进展，但合成具有照片级真实感和时间连贯性的动画帧序列仍处于初级阶段。虽然存在现成的数十亿规模图像生成数据集，但收集同等规模的视频数据仍具有挑战性。此外，训练视频扩散模型的计算成本远高于图像扩散模型。在这项工作中，我们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解决方案。我们发现，将图像噪声先验简单扩展为视频噪声先验会导致次优性能。我们精心设计的视频噪声先验带来了显著更好的性能。大量实验验证表明，我们的模型Preserve Your Own Correlation (PYoCo) 在UCF-101和MSR-VTT基准测试中获得了最先进的零样本文本到视频结果。它还在小规模UCF-101基准测试中，使用比先前方法小10倍的模型和显著更少的计算量，实现了最先进的视频生成质量。