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

• URL: http://arxiv.org/abs/2306.07954v2
• Date: Sun, 17 Sep 2023 09:57:20 GMT
• Title: Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation
• Authors: Shuai Yang, Yifan Zhou, Ziwei Liu and Chen Change Loy
• Abstract summary: This paper proposes a novel text-guided video-to-video translation framework to adapt image models to videos. Our framework achieves global style and local texture temporal consistency at a low cost.
• Score: 93.18163456287164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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