Recent advances in text-guided video editing have showcased promising results
in appearance editing (e.g., stylization). However, video motion editing in the
temporal dimension (e.g., from eating to waving), which distinguishes video
editing from image editing, is underexplored. In this work, we present UniEdit,
a tuning-free framework that supports both video motion and appearance editing
by harnessing the power of a pre-trained text-to-video generator within an
inversion-then-generation framework. To realize motion editing while preserving
source video content, based on the insights that temporal and spatial
self-attention layers encode inter-frame and intra-frame dependency
respectively, we introduce auxiliary motion-reference and reconstruction
branches to produce text-guided motion and source features respectively. The
obtained features are then injected into the main editing path via temporal and
spatial self-attention layers. Extensive experiments demonstrate that UniEdit
covers video motion editing and various appearance editing scenarios, and
surpasses the state-of-the-art methods. Our code will be publicly available.

UniEdit 是一个无需调整参数的框架，它利用预先训练的文本到视频生成器，通过反演和生成的方式来支持视频运动和外观编辑。通过引入辅助运动参考和重建分支，利用时间和空间自注意层来实现运动编辑并保留源视频内容，UniEdit 在视频运动编辑和各种外观编辑场景上优于现有的方法。