TokenFlow: Consistent Diffusion Features for Consistent Video Editing

Published: 16 Jan 2024, Last Modified: 11 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Computer Vision Applications, Video Editing and Synthesis, Text-to-Image Diffusion Models, Text-driven Video Editing
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Abstract: The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos.
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Primary Area: generative models
Submission Number: 3344