The remarkable efficacy of text-to-image diffusion models has motivated extensive exploration of their potential application in video domains. Zero-shot methods seek to extend image diffusion models to videos without necessitating model training. Recent methods mainly focus on incorporating inter-frame correspondence into attention mechanisms. However, the soft constraint imposed on determining where to attend to valid features can sometimes be insufficient, resulting in temporal inconsistency. In this paper, we introduce FRESCO, intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint. This enhancement ensures a more consistent transformation of semantically similar content across frames. Beyond mere attention guidance, our approach involves an explicit update of features to achieve high spatial-temporal consistency with the input video, significantly improving the visual coherence of the resulting translated videos. Extensive experiments demonstrate the effectiveness of our proposed framework in producing high-quality, coherent videos, marking a notable improvement over existing zero-shot methods.

我们介绍了一种名为FRESCO的方法，通过添加帧内和帧间对应关系建立了更强健的时空约束，在视频翻译中实现了语义相似内容的一致转换，显著改善了结果的视觉连贯性。大量实验证明了我们提出的框架在生成高质量、连贯的视频方面的有效性，相比现有的零样本方法有显著改进。

FRESCO: 零样本视频转换的时空对应关系