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.
@article{arxiv.2311.18827,
title = {Motion-Conditioned Image Animation for Video Editing},
author = {Wilson Yan and Andrew Brown and Pieter Abbeel and Rohit Girdhar and Samaneh Azadi},
journal= {arXiv preprint arXiv:2311.18827},
year = {2023}
}