Motion-Conditioned Image Animation for Video Editing

In arXiv, 2023

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

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.

Citation

@article{menon2023generating,
  author    = {Menon, Sachit and Misra, Ishan and Girdhar, Rohit},
  title     = {Generating Illustrated Instructions},
  journal   = {arXiv preprint arXiv:2312},
  year      = {2023},
}