  Existing diffusion-based video editing models have made gorgeous advances for
editing attributes of a source video over time but struggle to manipulate the
motion information while preserving the original protagonist's appearance and
background. To address this, we propose MotionEditor, a diffusion model for
video motion editing. MotionEditor incorporates a novel content-aware motion
adapter into ControlNet to capture temporal motion correspondence. While
ControlNet enables direct generation based on skeleton poses, it encounters
challenges when modifying the source motion in the inverted noise due to
contradictory signals between the noise (source) and the condition (reference).
Our adapter complements ControlNet by involving source content to transfer
adapted control signals seamlessly. Further, we build up a two-branch
architecture (a reconstruction branch and an editing branch) with a
high-fidelity attention injection mechanism facilitating branch interaction.
This mechanism enables the editing branch to query the key and value from the
reconstruction branch in a decoupled manner, making the editing branch retain
the original background and protagonist appearance. We also propose a skeleton
alignment algorithm to address the discrepancies in pose size and position.
Experiments demonstrate the promising motion editing ability of MotionEditor,
both qualitatively and quantitatively.
