StableV2V: Stablizing Shape Consistency in Video-to-Video Editing
Chang Liu, Rui Li, Kaidong Zhang, Yunwei Lan, Dong Liu
2024-11-19
Summary
This paper introduces StableV2V, a new method for video editing that ensures the shapes of objects remain consistent while making changes, improving the overall quality of edited videos.
What's the problem?
In video editing, especially when using AI, there is often a problem where the motion patterns from the original video do not align well with the edits made. This can lead to inconsistencies and poor quality in the final edited video, making it difficult to achieve the desired results based on user prompts.
What's the solution?
To solve this issue, StableV2V breaks down the video editing process into several steps. First, it edits the first frame of the video and then aligns the motions with what the user wants. After establishing this alignment, it applies the edits to all subsequent frames. Additionally, the authors created a testing benchmark called DAVIS-Edit to evaluate how well their method performs under different conditions and prompts. Their experiments show that StableV2V produces better quality videos with consistent shapes compared to existing methods.
Why it matters?
This research is important because it enhances video editing technology, making it more reliable and effective for users. By ensuring that shapes remain consistent while editing, StableV2V can improve applications in filmmaking, content creation, and other areas where high-quality video is essential.
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.