FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation
Shuai Yang Yifan Zhou Ziwei Liu Chen Change Loy
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.