Papers
arxiv:2306.07954

Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation

Published on Jun 13, 2023
· Submitted by akhaliq on Jun 14, 2023
#1 Paper of the day
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Abstract

Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.

Community

Is an implementation of this available yet? I'm really looking forward to trying this!

Would love to try this - great work!

How can I try this?

looks great!

Nice

any code?

@JackieLoong , the code will be released after the paper is published.

from the project website: https://anonymous-31415926.github.io/

Since there are no discussions I hope you let it here or maybe even add into readme

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How we can use this project to make animation

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