  Controllability plays a crucial role in video generation since it allows
users to create desired content. However, existing models largely overlooked
the precise control of camera pose that serves as a cinematic language to
express deeper narrative nuances. To alleviate this issue, we introduce
CameraCtrl, enabling accurate camera pose control for text-to-video(T2V)
models. After precisely parameterizing the camera trajectory, a plug-and-play
camera module is then trained on a T2V model, leaving others untouched.
Additionally, a comprehensive study on the effect of various datasets is also
conducted, suggesting that videos with diverse camera distribution and similar
appearances indeed enhance controllability and generalization. Experimental
results demonstrate the effectiveness of CameraCtrl in achieving precise and
domain-adaptive camera control, marking a step forward in the pursuit of
dynamic and customized video storytelling from textual and camera pose inputs.
Our project website is at: https://hehao13.github.io/projects-CameraCtrl/.
