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Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices (2405.12211v1)

Published 20 May 2024 in cs.CV

Abstract: Text-to-image (T2I) diffusion models achieve state-of-the-art results in image synthesis and editing. However, leveraging such pretrained models for video editing is considered a major challenge. Many existing works attempt to enforce temporal consistency in the edited video through explicit correspondence mechanisms, either in pixel space or between deep features. These methods, however, struggle with strong nonrigid motion. In this paper, we introduce a fundamentally different approach, which is based on the observation that spatiotemporal slices of natural videos exhibit similar characteristics to natural images. Thus, the same T2I diffusion model that is normally used only as a prior on video frames, can also serve as a strong prior for enhancing temporal consistency by applying it on spatiotemporal slices. Based on this observation, we present Slicedit, a method for text-based video editing that utilizes a pretrained T2I diffusion model to process both spatial and spatiotemporal slices. Our method generates videos that retain the structure and motion of the original video while adhering to the target text. Through extensive experiments, we demonstrate Slicedit's ability to edit a wide range of real-world videos, confirming its clear advantages compared to existing competing methods. Webpage: https://matankleiner.github.io/slicedit/

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Citations (3)
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Summary

  • The paper introduces Slicedit, a zero-shot video editing method that uses spatiotemporal slices to repurpose text-to-image diffusion models for video content.
  • It employs an inflated denoiser with extended attention to maintain temporal consistency and preserve structural details across frames.
  • Results demonstrate enhanced editing fidelity with higher CLIP scores and lower flow errors, showcasing its practical impact on dynamic video editing.

Zero-Shot Video Editing with Text-to-Image Diffusion Models: Unpacking Slicedit

Introduction

Text-to-image (T2I) diffusion models have transformed the way we generate and edit images using descriptive text prompts. While these models are highly effective for image synthesis, applying them to video editing has proven challenging, particularly when dealing with longer videos with complex motions. Traditional methods often stumble on maintaining temporal consistency. Enter Slicedit—a novel approach that ingeniously leverages T2I diffusion models for zero-shot video editing by incorporating spatiotemporal slices. Let's unpack how this works and what implications it holds for AI-driven video editing.

Key Concepts

The Challenge with Traditional Methods

Existing methods for video editing using T2I models usually involve some form of temporal consistency enforcement, but they often encounter difficulties:

  1. Temporal Inconsistencies: A naive frame-by-frame approach results in flickering and drift over time.
  2. Extended Attention: Some approaches employ extended attention across multiple frames but suffer from inconsistent texture and detail editing.
  3. Weak Correspondences: Methods using feature correspondence across frames may fail when dealing with fast or nonrigid motion.

The Innovation of Slicedit

Slicedit takes a different route by leveraging spatiotemporal slices. Here's the big idea:

  • Spatiotemporal Slices: These slices, which cut through the video in both space and time dimensions, display characteristics similar to natural images. This observation enables the use of pre-trained T2I diffusion models on these slices.
  • Inflated Denoiser: By modifying a T2I denoiser to process these slices alongside traditional frames, Slicedit can maintain temporal consistency better than existing methods.

How Slicedit Works

Slicedit employs a comprehensive process involving two main aspects: a customized denoiser and a thoughtful editing process.

The Inflated Denoiser

The core of Slicedit's approach lies in inflating the T2I denoiser to handle video:

  1. Extended Attention: The model extends attention to cover multiple video frames, which improves temporal consistency by capturing dynamics across frames.
  2. Spatiotemporal Processing: The denoiser is applied not only to individual frames but also to spatiotemporal slices. This multi-axis approach ensures that the model captures both spatial and temporal consistencies.

To achieve this, the denoiser, originally designed for images, is modified to handle video by incorporating spatiotemporal slices and extended attention mechanisms. The result is a combined video denoiser that merges the outcomes from both frame-based and spatiotemporal processing.

Editing Process

The editing process in Slicedit is divided into two stages:

  1. Inversion: This stage involves generating noisy versions of the input video frames and extracting the noise vectors for each timestep.
  2. Sampling: During the sampling stage, the noise vectors are used to regenerate the video, conditioned on the new text prompt. Features from the source video's extended attention maps are injected to maintain structural consistency.

The result is a video that adheres to the new text prompt while preserving the original motion and structure.

Numerical Results

Slicedit's performance was evaluated on a diverse set of videos, and the results were promising:

  • Editing Fidelity: Measured using the CLIP score, Slicedit demonstrated strong adherence to the target text prompt.
  • Temporal Consistency: With lower flow errors compared to other methods, Slicedit showed superior handling of motion across frames.
  • Preservation of Structure: The LPIPS score indicated that Slicedit effectively preserved the structure and appearance of the unedited regions.

Implications and Future Directions

Slicedit's approach of leveraging spatiotemporal slices opens new possibilities in AI-driven video editing:

  • Practical Applications: This method can be used in various creative fields, from filmmaking to advertising, where quick, high-quality video edits are valuable.
  • Theoretical Insights: The use of spatiotemporal slices suggests new avenues for improving other machine learning models by combining spatial and temporal information.
  • Future Developments: There is potential for further refinement, such as more advanced techniques for maintaining temporal consistency or adapting the method for even longer videos.

Conclusion

Slicedit represents a significant step forward in zero-shot video editing using text-to-image diffusion models. By incorporating spatiotemporal slices and an inflated denoiser approach, it addresses key challenges in maintaining temporal consistency and preserving structure in video edits. While not without limitations, such as its current inability to handle more drastic edits (like transforming a dog into an elephant), Slicedit offers a robust foundation for future innovations in video editing technology.

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