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。

编辑你的动作：时空扩散解耦学习用于视频动作编辑

Edit-Your-Motion: Space-Time Diffusion Decoupling Learning for Video  Motion Editing

The thesis develops a view of design in a concept formation framework and
outlines a language to describe both the object of the design and the process
of designing. The unknown object at the outset of the design work may be seen
as an unknown concept that the designer is to define. Throughout the process,
she develops a description of this object by relating it to known concepts. The
search stops when the designer is satisfied that the design specification is
complete enough to satisfy the requirements from it once built. It is then a
collection of propositions that all contribute towards defining the design
object - a collection of sentences describing relationships between the object
and known concepts. Also, the design process itself may be described by
relating known concepts - by organizing known abilities into particular
patterns of activation, or mobilization. In view of the demands posed to a
language to use in this concept formation process, the framework of a Design
Process Language (DPL) is developed. The basis for the language are linguistic
categories that act as classes of relations used to combine concepts,
containing relations used for describing process and object within the same
general system, with some relations being process specific, others being object
specific, and with the bulk being used both for process and object description.
Another outcome is the distinction of modal relations, or relations describing
futurity, possibility, willingness, hypothetical events, and the like. The
design process almost always includes aspects such as these, and it is thus
necessary for a language facilitating design process description to support
such relationships to be constructed. The DPL is argued to be a foundation
whereupon to build a language that can be used for enabling computers to be
more useful - act more intelligently - in the design process.

设计过程中，通过概念形成框架和语言描述设计目标和设计过程，并开发了一个设计过程语言（DPL），用于构建一个更有用、更智能的计算机设计过程。