MotionMaster: Training-free Camera Motion Transfer For Video Generation

• URL: http://arxiv.org/abs/2404.15789v2
• Date: Wed, 1 May 2024 02:37:18 GMT
• Title: MotionMaster: Training-free Camera Motion Transfer For Video Generation
• Authors: Teng Hu, Jiangning Zhang, Ran Yi, Yating Wang, Hongrui Huang, Jieyu Weng, Yabiao Wang, Lizhuang Ma,
• Abstract summary: We propose a novel training-free video motion transfer model, which disentangles camera motions and object motions in source videos. Our model can effectively decouple camera-object motion and apply the decoupled camera motion to a wide range of controllable video generation tasks.
• Score: 48.706578330771386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of diffusion models has greatly propelled the progress in image and video generation. Recently, some efforts have been made in controllable video generation, including text-to-video generation and video motion control, among which camera motion control is an important topic. However, existing camera motion control methods rely on training a temporal camera module, and necessitate substantial computation resources due to the large amount of parameters in video generation models. Moreover, existing methods pre-define camera motion types during training, which limits their flexibility in camera control. Therefore, to reduce training costs and achieve flexible camera control, we propose COMD, a novel training-free video motion transfer model, which disentangles camera motions and object motions in source videos and transfers the extracted camera motions to new videos. We first propose a one-shot camera motion disentanglement method to extract camera motion from a single source video, which separates the moving objects from the background and estimates the camera motion in the moving objects region based on the motion in the background by solving a Poisson equation. Furthermore, we propose a few-shot camera motion disentanglement method to extract the common camera motion from multiple videos with similar camera motions, which employs a window-based clustering technique to extract the common features in temporal attention maps of multiple videos. Finally, we propose a motion combination method to combine different types of camera motions together, enabling our model a more controllable and flexible camera control. Extensive experiments demonstrate that our training-free approach can effectively decouple camera-object motion and apply the decoupled camera motion to a wide range of controllable video generation tasks, achieving flexible and diverse camera motion control.

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