Motion-Conditioned Image Animation for Video Editing

Wilson Yan; Andrew Brown; Pieter Abbeel; Rohit Girdhar; Samaneh Azadi

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arXiv · 2311.18827

Motion-Conditioned Image Animation for Video Editing

Wilson Yan·Andrew Brown·Pieter Abbeel·Rohit Girdhar·Samaneh Azadi

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Abstract

We introduce MoCA, a Motion-Conditioned Image Animation approach for video editing. It leverages a simple decomposition of the video editing problem into image editing followed by motion-conditioned image animation. Furthermore, given the lack of robust evaluation datasets for video editing, we introduce a new benchmark that measures edit capability across a wide variety of tasks, such as object replacement, background changes, style changes, and motion edits. We present a comprehensive human evaluation of the latest video editing methods along with MoCA, on our proposed benchmark. MoCA establishes a new state-of-the-art, demonstrating greater human preference win-rate, and outperforming notable recent approaches including Dreamix (63%), MasaCtrl (75%), and Tune-A-Video (72%), with especially significant improvements for motion edits.

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cs.GR cs.AI cs.CV cs.LG cs.MM

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BibTeX RIS

Wilson Yan, Andrew Brown, Pieter Abbeel, Rohit Girdhar, Samaneh Azadi. 2023-11-30. Motion-Conditioned Image Animation for Video Editing. https://arxiv.org/abs/2311.18827

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