Existing diffusion-based video editing methods have achieved impressive
results in motion editing. Most of the existing methods focus on the motion
alignment between the edited video and the reference video. However, these
methods do not constrain the background and object content of the video to
remain unchanged, which makes it possible for users to generate unexpected
videos. In this paper, we propose a one-shot video motion editing method called
Edit-Your-Motion that requires only a single text-video pair for training.
Specifically, we design the Detailed Prompt-Guided Learning Strategy (DPL) to
decouple spatio-temporal features in space-time diffusion models. DPL separates
learning object content and motion into two training stages. In the first
training stage, we focus on learning the spatial features (the features of
object content) and breaking down the temporal relationships in the video
frames by shuffling them. We further propose Recurrent-Causal Attention
(RC-Attn) to learn the consistent content features of the object from unordered
video frames. In the second training stage, we restore the temporal
relationship in video frames to learn the temporal feature (the features of the
background and object's motion). We also adopt the Noise Constraint Loss to
smooth out inter-frame differences. Finally, in the inference stage, we inject
the content features of the source object into the editing branch through a
two-branch structure (editing branch and reconstruction branch). With
Edit-Your-Motion, users can edit the motion of objects in the source video to
generate more exciting and diverse videos. Comprehensive qualitative
experiments, quantitative experiments and user preference studies demonstrate
that Edit-Your-Motion performs better than other methods.

通过提出一种一次性视频运动编辑方法（Edit-Your-Motion），利用详细提示引导学习策略（DPL）对时空扩散模型进行空间 - 时间特征解耦，从而实现对视频对象内容和运动的编辑。该方法比其他方法表现更好，能够生成更加激动人心和多样化的视频。