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

**Authors:** Shuai Yang, Yifan Zhou, Ziwei Liu, Chen Change Loy  
**arXiv:** [2306.07954](https://arxiv.org/abs/2306.07954)  
**Date:** 2023-06-13  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2306.07954v1/

## Key points

- Text-to-image diffusion models have made significant strides in generating high-quality images.
- Applying these models to video is challenging due to ensuring temporal consistency across frames.
- A team of researchers has proposed a novel zero-shot text-guided video-to-video translation framework that adapts image models to videos.
- The framework consists of two parts: key frame translation and full video translation.
- The first part generates key frames while enforcing coherence in shapes, textures and colors through hierarchical cross-frame constraints using an adapted diffusion model.
- The second part propagates the key frames to other frames using temporal-aware patch matching and frame blending techniques.
- The proposed framework achieves global style and local texture temporal consistency at a low cost without requiring re-training or optimization.
- This adaptation is compatible with existing image diffusion techniques such as customizing specific subjects with LoRA or introducing extra spatial guidance with ControlNet.
- Experimental results demonstrate the effectiveness of the proposed framework over existing methods in rendering high quality and temporally coherent videos.
- This approach has significant implications for various applications such as video editing and content creation where maintaining visual coherence across frames is critical for producing high quality outputs.

## Summary

The field of text-to-image diffusion models has made significant strides in generating high-quality images. However, when it comes to applying these models to video, ensuring temporal consistency across frames remains a challenging task. To address this issue, a team of researchers comprising Shuai Yang, Yifan Zhou, Ziwei Liu and Chen Change Loy have proposed a novel zero-shot text-guided video-to-video translation framework that adapts image models to videos. The framework consists of two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames while enforcing coherence in shapes, textures and colors through hierarchical cross-frame constraints. The second part propagates the key frames to other frames using temporal-aware patch matching and frame blending techniques. Importantly, the proposed framework achieves global style and local texture temporal consistency at a low cost without requiring re-training or optimization. One notable advantage of this adaptation is its compatibility with existing image diffusion techniques such as customizing specific subjects with LoRA or introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of the proposed framework over existing methods in rendering high quality and temporally coherent videos. Overall, this paper presents an innovative approach that addresses the challenge of achieving temporal consistency in video generation by leveraging text guided adaptation techniques from image models. This work has significant implications for various applications such as video editing and content creation where maintaining visual coherence across frames is critical for producing high quality outputs.