  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.
