MagicStick: Controllable Video Editing via Control Handle Transformations

Yue Ma, Xiaodong Cun, Sen Liang, Jinbo Xing, Yingqing He, Chenyang Qi, Siran Chen, Qifeng Chen; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 9367-9377

Text-based video editing has recently attracted considerable interest in changing the style or replacing the objects with a similar structure. Beyond this we demonstrate that properties such as shape size location motion etc. can also be edited in videos. Our key insight is that the keyframe's transformations of the specific internal feature (e.g. edge maps of objects or human pose) can easily propagate to other frames to provide generation guidance. We thus propose MagicStick a controllable video editing method that edits the video properties by utilizing the transformation on the extracted internal control signals. In detail to keep the appearance we inflate both the pre-trained image diffusion model and ControlNet to the temporal dimension and train low-rank adaptions (LoRA) layers to fit the specific scenes. Then in editing we perform an inversion and editing framework. Differently finetuned ControlNet is introduced in both inversion and generation for attention guidance with the proposed attention remix between the spatial attention maps of inversion and editing. Yet succinct our method is the first method to show the ability of video property editing from the pre-trained text-to-image model. We present experiments on numerous examples within our unified framework. We also compare with shape-aware text-based editing and handcrafted motion video generation demonstrating our superior temporal consistency and editing capability than previous works.