Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation (2306.07954v2)

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.

Zero-Shot Text-Guided Video-to-Video Translation: A Comprehensive Framework

The manuscript presents an advanced framework for addressing the challenges of incorporating text-guided image diffusion models into video translation tasks. This work addresses a significant challenge in video-to-video translation: ensuring temporal consistency across successive frames without the need for retraining, leveraging advancements in image diffusion models.

Overview and Methodology

The paper introduces a zero-shot text-guided video-to-video translation framework that adapts image diffusion models to address the particular requirements of video. The proposed framework is segmented into two phases: key frame translation and full video translation. The key frame translation relies on an adapted diffusion model that incorporates hierarchical cross-frame constraints designed to ensure coherence in shape, texture, and color across frames. For full video translation, the technique propagates key frames using temporal-aware patch matching and frame blending to integrate and blend styles throughout the video sequence efficiently.

Key Innovations

1. Temporal Coherence: By introducing a method that applies hierarchical cross-frame constraints at varying stages of the diffusion process, the proposed framework emphasizes maintaining both global and local temporal consistency in the resulting translated videos.
2. Zero-Shot Framework: Arguably, one of the most critical features is its zero-shot capability—this framework does not require retraining or optimization, making it an efficient and resource-conscious approach.
3. Compatibility with Existing Models: The adaptation works harmoniously with extant image diffusion models, allowing it to utilize such models’ features extensively, such as subject customization with LoRA and spatial guidance with ControlNet.
4. Fidelity-Oriented Image Encoding: To address information loss due to lossy autoencoding, the paper introduces a fidelity-oriented image encoding methodology, which estimates and compensates for encoding losses to preserve rich texture and color fidelity across frames.

Experimental Results

The results obtained from extensive experiments indicate that the proposed framework offers superior performance compared to existing methods concerning high-quality and temporally-consistent video rendering. The key innovation to achieve pixel-level temporal consistency marks a departure from prior efforts that were confined to ensuring only global style consistency.

Implications and Future Directions

The implications of this work span practical and theoretical realms:

• Practical Applications: Facilitating a smoother and more coherent transition from text-guided image generation to video domain applications can notably impact content creation industries. It can streamline video editing and stylization, enhance creative workflows, and reduce the computational load traditionally associated with such tasks.
• Theoretical Advancements: Introducing novel ways to align image and video diffusion phenomena opens avenues for improving adaptability of models across different yet related tasks. The hierarchical cross-frame constraints and fidelity-oriented encoding can inspire future research into adaptive usage of pre-trained models for various multimedia applications.

Looking forward, further work could explore expanding the approach to more diverse video genres and broader artistic expressions, in addition to refining techniques to handle challenging scenarios such as extreme movements or occlusions. The seamless integration of such frameworks with emerging AI tools in interactive multimedia presents exciting opportunities for comprehensive content generation and manipulation systems in AI research.