StableV2V: Stablizing Shape Consistency in Video-to-Video Editing (2411.11045v1)

Abstract: Recent advancements of generative AI have significantly promoted content creation and editing, where prevailing studies further extend this exciting progress to video editing. In doing so, these studies mainly transfer the inherent motion patterns from the source videos to the edited ones, where results with inferior consistency to user prompts are often observed, due to the lack of particular alignments between the delivered motions and edited contents. To address this limitation, we present a shape-consistent video editing method, namely StableV2V, in this paper. Our method decomposes the entire editing pipeline into several sequential procedures, where it edits the first video frame, then establishes an alignment between the delivered motions and user prompts, and eventually propagates the edited contents to all other frames based on such alignment. Furthermore, we curate a testing benchmark, namely DAVIS-Edit, for a comprehensive evaluation of video editing, considering various types of prompts and difficulties. Experimental results and analyses illustrate the outperforming performance, visual consistency, and inference efficiency of our method compared to existing state-of-the-art studies.

• The paper introduces a novel pipeline that decomposes video editing into first-frame editing, motion-propagation alignment, and global adjustment to ensure shape consistency.
• It leverages a three-component approach—PFE, ISA, and CIG—using depth maps, optical flows, and segmentation masks to align edits with user prompts.
• Experimental validation on the DAVIS-Edit benchmark shows significant improvements in DOVER and FVD metrics over existing methods, enhancing temporal coherence.

Stabilizing Shape Consistency in Video-to-Video Editing: An Overview of StableV2V

The emergence of generative AI has brought significant advancements in the field of content creation, expanding its influential reach to video editing. Despite this progress, a critical challenge persists in the domain: achieving shape consistency between edited video content and user prompts. The paper "StableV2V: Stabilizing Shape Consistency in Video-to-Video Editing" addresses this challenge by proposing a novel methodology for ensuring shape-consistent video editing, separating itself from previous attempts through a novel composition of components and testing frameworks.

The StableV2V Methodology

The StableV2V framework revolutionizes existing paradigms by decomposing the video editing pipeline into distinct procedural steps: first-frame editing, motion-propagation alignment, and subsequent global frame adjustment. By starting with the creation of a coherent alignment between initial frame edits and user prompts and then extending these edits seamlessly across subsequent frames, StableV2V ensures video edits maintain user-defined motions and shapes consistently. This process is accomplished through three primary components: the Prompted First-frame Editor (PFE), the Iterative Shape Aligner (ISA), and the Conditional Image-to-video Generator (CIG).

• Prompted First-frame Editor (PFE): This acts as the initial step wherein the first frame of the video is edited based on external prompts varied in form, such as text, images, or instructions. This serves as the keystone for subsequent aligned editing.
• Iterative Shape Aligner (ISA): The ISA component aligns motion propagation of the edited first frame with the original video content by utilizing depth maps, optical flows, and segmentation masks. It adeptly handles interim features including simulated optical flows and shape-guided refinement networks, ensuring shape consistency throughout the video.
• Conditional Image-to-video Generator (CIG): Guided by the refined depth maps from ISA, CIG finalizes the video generation, comprehensively adapting the initial frame's motion and style transformations across all frames of the video.

Experimental Validation and Results

A pivotal contribution of the paper lies in its introduction of a comprehensive evaluation benchmark, DAVIS-Edit, specifically designed to assess the efficacy of video editing techniques under diverse prompt categories and difficulty levels. Through rigorous testing, StableV2V demonstrates superior performance over existing state-of-the-art approaches in terms of visual consistency and computational efficiency. For instance, the method significantly improves on the DOVER score and FVD metrics compared to competitors like AnyV2V and DMT, highlighting its advancement in maintaining temporal coherence and shape fidelity in video edits.

Implications and Future Directions

StableV2V's contribution hinges on its ability to deliver shape-consistent, visually coherent video edits, addressing a prominent limitation in the existing methodologies. Practically, it paves the way for more reliable generative video editing processes in creative industries, where precision in content transformation is paramount. The framework significantly enhances the adaptability of video editing tools to varied user inputs while maintaining computational efficiency.

Theoretically, the innovation in decomposing and aligning video motion and shape edits provides a fresh vista for further research. The method's modular design harbors potential for refinement and integration with other AI advancements, such as more sophisticated deep models or enriched datasets. A future trajectory could focus on enhancing the expressiveness and robustness of the ISA module for even more complex shape manipulation and motion alignment scenarios.

In summary, StableV2V marks a notable progression in the quest for more sophisticated video editing tools, with its methodology offering a structured approach to shape-consistent video editing—a crucial consideration for real-world application across diverse multimedia and content generation industries.