@inproceedings{
hu2024comd,
title={{COMD}: Training-free Video Motion Transfer With Camera-Object Motion Disentanglement},
author={Teng Hu and Jiangning Zhang and Ran Yi and Yating Wang and Jieyu Weng and Hongrui Huang and Yabiao Wang and Lizhuang Ma},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=oHF1aMP5p2}
}
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, image-to-video generation, video editing, 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, preventing the realization of some specific camera controls, such as various camera movements in films. 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.
COMD: Training-free Video Motion Transfer With Camera-Object Motion Disentanglement
Teng Hu Jiangning Zhang Ran Yi Yating Wang Jieyu Weng Hongrui Huang Yabiao Wang Lizhuang Ma