The development of Text-to-Video (T2V) generation has made motion transfer possible, enabling the control of video motion based on existing footage. However, current techniques struggle to preserve the diversity and accuracy of motion while transferring it to subjects with varying shapes. Additionally, they fail to handle videos with multiple subjects, making it difficult to disentangle and control motion transfer to specific instances.To overcome these challenges, we introduce ConMo, a training-free framework that disentangle and recompose the motions of subjects and camera movements. Unlike existing methods, ConMo separates individual subject motions and background motions from compound motion trajectories in complex videos, allowing for precise control and flexible manipulation, such as resizing and repositioning. This leads to more accurate motion transfer across subjects of different shapes and better handling of videos with multiple subjects. Extensive experiments demonstrate that ConMo can effectively achieve the aforementioned various applications and surpass existing state-of-the-art methods in terms of motion fidelity and temporal consistency. The code will be published.