Despite impressive advancements in diffusion-based video editing models in altering video attributes, there has been limited exploration into modifying motion information while preserving the original protagonist's appearance and background. In this paper, we propose MotionFollower, a lightweight score-guided diffusion model for video motion editing. To introduce conditional controls to the denoising process, MotionFollower leverages two of our proposed lightweight signal controllers, one for poses and the other for appearances, both of which consist of convolution blocks without involving heavy attention calculations. Further, we design a score guidance principle based on a two-branch architecture, including the reconstruction and editing branches, which significantly enhance the modeling capability of texture details and complicated backgrounds. Concretely, we enforce several consistency regularizers and losses during the score estimation. The resulting gradients thus inject appropriate guidance to the intermediate latents, forcing the model to preserve the original background details and protagonists' appearances without interfering with the motion modification. Experiments demonstrate the competitive motion editing ability of MotionFollower qualitatively and quantitatively. Compared with MotionEditor, the most advanced motion editing model, MotionFollower achieves an approximately 80% reduction in GPU memory while delivering superior motion editing performance and exclusively supporting large camera movements and actions.

翻译：尽管基于扩散的视频编辑模型在改变视频属性方面取得了显著进展，但如何在保持原始主体外观和背景的同时修改运动信息的研究仍然有限。本文提出MotionFollower，一种用于视频运动编辑的轻量级分数引导扩散模型。为了在去噪过程中引入条件控制，MotionFollower利用我们提出的两个轻量级信号控制器——分别针对姿态和外观，两者均采用卷积块构建，无需繁重的注意力计算。进一步，我们基于包含重建分支和编辑分支的双分支架构设计了分数引导机制，显著增强了对纹理细节和复杂背景的建模能力。具体而言，我们在分数估计过程中施加了多种一致性正则化项和损失函数，由此产生的梯度被注入中间隐变量，迫使模型在实现运动修改的同时保持原始背景细节和主体外观。实验从定性和定量角度证明了MotionFollower具有竞争力的运动编辑能力。与最先进的运动编辑模型MotionEditor相比，MotionFollower在实现更优运动编辑性能、独家支持大幅相机运动和动作的同时，GPU内存占用降低了约80%。