StableV2V: Stabilizing Shape Consistency in Video-to-Video Editing

作者：Chang Liu, Rui Li, Kaidong Zhang, Yunwei Lan, Dong Liu · 发表于：IEEE Transactions on Circuits and Systems for Video Technology · 年份：2025 · DOI：10.1109/tcsvt.2025.3639307 · 被引用次数：1 · 研究领域：Image and Video Stabilization、Advanced Vision and Imaging、Generative Adversarial Networks and Image Synthesis

Recent advancements in generative artificial intelligence have significantly promoted content creation and editing, where prevailing studies further extend this exciting progress to video editing. These studies mainly transfer the inherent motion patterns from the source videos to the edited ones, where they often produce inferior results with inconsistency to user intentions, especially when shape changes between the edited and original objects might occur, due to the lack of particular alignments between the delivered motions and edited content. To address this limitation, we present a shape-consistent video editing method, namely StableV2V. Our method decomposes the entire editing pipeline into several sequential procedures, where we first edit the initial video frame, then simulate the shape-aware alignment between the delivered motions and edited sequence, and propagate the edited content to all other frames based on such alignment. Furthermore, we curate a testing benchmark, namely DAVIS-Edit, to offer a comprehensive evaluation of video editing, considering various types of prompts and difficulties. Experimental results and analyses illustrate the superior performance, visual consistency, and inference efficiency of our proposed method compared to existing state-of-the-art video editing studies.