MagicProp: Diffusion-based Video Editing via Motion-aware Appearance
  Propagation
Hanshu Yan Jun Hao Liew Long Mai Shanchuan Lin Jiashi Feng
This paper addresses the issue of modifying the visual appearance of videos
while preserving their motion. A novel framework, named MagicProp, is proposed,
which disentangles the video editing process into two stages: appearance
editing and motion-aware appearance propagation. In the first stage, MagicProp
selects a single frame from the input video and applies image-editing
techniques to modify the content and/or style of the frame. The flexibility of
these techniques enables the editing of arbitrary regions within the frame. In
the second stage, MagicProp employs the edited frame as an appearance reference
and generates the remaining frames using an autoregressive rendering approach.
To achieve this, a diffusion-based conditional generation model, called
PropDPM, is developed, which synthesizes the target frame by conditioning on
the reference appearance, the target motion, and its previous appearance. The
autoregressive editing approach ensures temporal consistency in the resulting
videos. Overall, MagicProp combines the flexibility of image-editing techniques
with the superior temporal consistency of autoregressive modeling, enabling
flexible editing of object types and aesthetic styles in arbitrary regions of
input videos while maintaining good temporal consistency across frames.
Extensive experiments in various video editing scenarios demonstrate the
effectiveness of MagicProp.