StableV2V: Stablizing Shape Consistency in Video-to-Video Editing
Authors: name: Chang Liu, name: Rui Li, name: Kaidong Zhang, name: Yunwei Lan, name: Dong Liu

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.