FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation
(2403.12962)Abstract
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.
Overview
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FRESCO introduces a framework for zero-shot video translation that embeds spatial-temporal correspondence to improve visual coherence.
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The methodology includes feature and attention adaptation to focus on valid features and enhance coherence across video frames.
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Quantitative assessments and qualitative user studies demonstrate FRESCO's superiority in editing accuracy and temporal consistency over existing methods.
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FRESCO's integration with techniques like ControlNet, SDEdit, and LoRA extends its applicability, offering more versatile video manipulation without model retraining.
Enhancing Zero-Shot Video Translation through Spatial-Temporal Correspondence with FRESCO
Introduction to FRESCO
In the landscape of video editing and translation, maintaining spatial-temporal consistency presents a significant challenge, particularly when short videos have become a staple of digital entertainment. Existing methods often struggle with achieving coherence, especially in the context of manipulating videos through text prompts without extensive model training. FRESCO (FRamE Spatial-temporal COrrespondence) introduces an innovative framework aimed at addressing these challenges by embedding both intra-frame spatial and inter-frame temporal correspondence. This approach significantly improves the visual coherence of the output, setting a new benchmark for zero-shot video translation methods.
Core Contributions
The paper delineates FRESCO's primary contributions as follows:
- Introduction of a zero-shot video translation framework that employs spatial-temporal guidance to achieve high-quality, consistent translations.
- Integration of FRESCO-guided feature attention and optimization as a robust method for enhancing both spatial and temporal consistency beyond what is achievable through optical flow alone.
- A strategy for translating longer videos by processing batched frames in a manner that ensures consistency across batches through shared anchor frames.
Methodology
FRESCO's methodology revolves around two main adaptations: feature adaptation and attention adaptation. Feature adaptation involves direct optimization of U-Net decoder layer features to enhance their coherence with the input frames concerning both time and space. Attention adaptation, on the other hand, replaces standard self-attentions with FRESCO-guided attentions, which comprises spatial-guided, efficient cross-frame, and temporal-guided attention mechanisms. This multi-pronged adaptation effectively directs the focus towards valid features, ensuring a more constrained translation that honors the original video content's spatial-temporal integrity.
Performance Evaluation
Extensive experiments highlight FRESCO's superiority over existing zero-shot methods. Quantitatively, FRESCO demonstrates notable improvements in editing accuracy and temporal consistency, as evidenced by leading scores in Frame-Acc, Temp-Con, and Pixel-MSE metrics. Qualitatively, user studies further reinforce FRESCO's effectiveness, with a significant majority of participants favoring FRESCO's output due to its enhanced coherence and visual quality.
Theoretical and Practical Implications
The introduction of FRESCO opens new pathways for research and practical applications in the field of video editing. Theoretically, it underlines the importance of integrating spatial and temporal correspondence in maintaining coherence in video translation. Practically, its compatibility with various assistive techniques, like ControlNet, SDEdit, and LoRA, promises more versatile and customizable video manipulation tools for users without necessitating model retraining or specialized datasets.
Future Directions
While FRESCO marks a significant advance, the study acknowledges room for improvement and potential future directions. These include exploring a hybrid approach that combines pixel-level alignment with FRESCO's correspondence model for even finer coherence and extending the framework to accommodate significant shape deformations and appearance changes, a current limitation due to its reliance on pre-existing video motion patterns.
Conclusion
FRESCO represents a milestone in zero-shot video translation, offering a robust solution to the long-standing problem of maintaining spatial-temporal consistency. By leveraging the inherent semantics within and across video frames, FRESCO not only enhances the visual coherence of translated videos but also provides a solid foundation for future advancements in video editing technology.
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