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.

MoCA 是一种基于动作条件的图像动画方法，用于视频编辑，并通过图像编辑和动作条件的图像动画简化了视频编辑问题。此外，我们还提出了一个新的基准测试，用于测量对象替换、背景变化、风格变化和动作编辑等各种任务的编辑能力，并在我们提出的基准测试上对最新的视频编辑方法以及 MoCA 进行了全面的人工评估。MoCA 建立了一个新的最先进技术，展示了更高的人类偏好获胜率，并且在动作编辑方面胜过了包括 Dreamix（63%）、MasaCtrl（75%）和 Tune-A-Video（72%）在内的值得注意的最近方法，带来了显著的改进。