Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models (2303.17599v3)

Abstract: Large-scale text-to-image diffusion models achieve unprecedented success in image generation and editing. However, how to extend such success to video editing is unclear. Recent initial attempts at video editing require significant text-to-video data and computation resources for training, which is often not accessible. In this work, we propose vid2vid-zero, a simple yet effective method for zero-shot video editing. Our vid2vid-zero leverages off-the-shelf image diffusion models, and doesn't require training on any video. At the core of our method is a null-text inversion module for text-to-video alignment, a cross-frame modeling module for temporal consistency, and a spatial regularization module for fidelity to the original video. Without any training, we leverage the dynamic nature of the attention mechanism to enable bi-directional temporal modeling at test time. Experiments and analyses show promising results in editing attributes, subjects, places, etc., in real-world videos. Code is made available at \url{https://github.com/baaivision/vid2vid-zero}.

Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models

The paper "Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models" introduces a method referred to as vid2vid-zero, which addresses the challenge of video editing without requiring any additional training on video data. Building on the capabilities of large-scale text-to-image diffusion models like DALL·E 2 and Stable Diffusion, the authors propose an innovative approach to extend these models' utility from static images to dynamic video content.

Methodology

Vid2vid-zero functions based on three core modules: a null-text inversion module for aligning the video with text prompts, a spatial regularization module to maintain fidelity to the original video content, and a cross-frame modeling module to ensure temporal consistency.

1. Null-Text Inversion: This component modifies the null-text embedding to facilitate alignment between the video's inherent content and the text prompt. The inversion process leverages DDIM inversion to map video frames into noise space while re-aligning them with the prompt, ensuring text-to-video coherence.
2. Cross-Frame Modeling: Temporal consistency, a quintessential characteristic of coherent video editing, is achieved through a spatial-temporal attention mechanism. By adopting a dense spatial-temporal attention approach, bidirectional temporal modeling is enabled at test time. This improves temporal fidelity by allowing the framework to dynamically factor in both past and future frames without additional training, aiding in the successful editing of real-world videos.
3. Spatial Regularization: Cross-attention maps are employed as guidance during the editing process to maintain the video's spatial fidelity. The approach injects these maps into the pre-trained model to regulate frame-wise editing, ensuring that the output remains faithful to the original video structure.

Experimental Results

The method is rigorously evaluated through both qualitative and quantitative measures, illustrating the effectiveness of the zero-shot editing technique. Vid2vid-zero excels notably in several editing dimensions, including style, attribute, subject replacement, and background alteration:

• Style Transfer: The method successfully applies styles such as "anime" to large sections of video while preserving content integrity.
• Attribute Modification: Demonstrating the ability to alter specific features, vid2vid-zero changes details like age or object models within videos, aligning with textual prompts while retaining most of the original scene attributes.
• Background Alteration and Subject Replacement: The approach effectively alters backgrounds and replaces subjects to match new semantic prompts, maintaining high temporal and spatial coherence.

When compared to alternative methods such as Tune-A-Video and Plug-and-Play techniques, vid2vid-zero achieves superior results in alignment with target prompts and temporal consistency, confirmed by user studies and quantitative metrics like CLIP scores and frame consistency metrics.

Implications and Future Prospects

Vid2vid-zero represents a significant step towards more accessible and flexible video editing by exploiting the emergent capabilities of pre-trained image diffusion models, negating the need for fine-tuning on expansive video datasets. However, it is constrained by inherent limitations in addressing motion dynamics or verb-focused edits, as video-to-image carryover techniques lack intrinsic temporal priors.

The research provides a foundation for exploring further enhancements in zero-shot model adaptability and the integration of video-specific learning frameworks. Future developments may consider incorporating video datasets to instil temporal motion priors, thereby refining action-focused video edits. The potential to extend such methodologies toward adaptive real-time video applications could redefine expectations in multimedia content creation and editing fields.