CameraCtrl enables precise camera control for text-to-video generation. The paper introduces a plug-and-play camera control module that can be integrated into existing text-to-video (T2V) models to achieve accurate and domain-adaptive camera control. The module uses plücker embeddings to represent camera parameters, which provide a geometric interpretation for each pixel in a video frame, enabling more precise control over camera viewpoints. The module is trained on a diverse dataset with a wide range of camera poses and similar appearance to the base T2V model, achieving a good balance between generalizability and controllability. Experimental results show that CameraCtrl can generate videos with precise camera control, demonstrating its effectiveness in enhancing the realism and customization of generated videos. The module is also compatible with other video generation control techniques, such as SparseCtrl, and can be applied to various video domains, including natural scenes, stylized objects, and cartoon characters. The paper also presents an ablation study on the impact of different datasets and model architectures on camera control performance. Overall, CameraCtrl provides a flexible and effective solution for controlling camera viewpoints in text-to-video generation.CameraCtrl enables precise camera control for text-to-video generation. The paper introduces a plug-and-play camera control module that can be integrated into existing text-to-video (T2V) models to achieve accurate and domain-adaptive camera control. The module uses plücker embeddings to represent camera parameters, which provide a geometric interpretation for each pixel in a video frame, enabling more precise control over camera viewpoints. The module is trained on a diverse dataset with a wide range of camera poses and similar appearance to the base T2V model, achieving a good balance between generalizability and controllability. Experimental results show that CameraCtrl can generate videos with precise camera control, demonstrating its effectiveness in enhancing the realism and customization of generated videos. The module is also compatible with other video generation control techniques, such as SparseCtrl, and can be applied to various video domains, including natural scenes, stylized objects, and cartoon characters. The paper also presents an ablation study on the impact of different datasets and model architectures on camera control performance. Overall, CameraCtrl provides a flexible and effective solution for controlling camera viewpoints in text-to-video generation.