Driven by the upsurge progress in text-to-image (T2I) generation models,
text-to-video (T2V) generation has experienced a significant advance as well.
Accordingly, tasks such as modifying the object or changing the style in a
video have been possible. However, previous works usually work well on trivial
and consistent shapes, and easily collapse on a difficult target that has a
largely different body shape from the original one. In this paper, we spot the
bias problem in the existing video editing method that restricts the range of
choices for the new protagonist and attempt to address this issue using the
conventional image-level personalization method. We adopt motion
personalization that isolates the motion from a single source video and then
modifies the protagonist accordingly. To deal with the natural discrepancy
between image and video, we propose a motion word with an inflated textual
embedding to properly represent the motion in a source video. We also regulate
the motion word to attend to proper motion-related areas by introducing a novel
pseudo optical flow, efficiently computed from the pre-calculated attention
maps. Finally, we decouple the motion from the appearance of the source video
with an additional pseudo word. Extensive experiments demonstrate the editing
capability of our method, taking a step toward more diverse and extensive video
editing.

本文提出了一种使用传统的图像层面个性化方法解决现有视频编辑方法中的偏见问题，通过运动个性化从单一源视频中分离运动并相应地修改主角，并引入一种新颖的伪光流来调节运动词以适应运动相关区域，最终通过额外的伪词将运动与源视频的外观分离，从而实现了更多样化和广泛的视频编辑能力。