Camera Control in AI Video Generation

Camera control comes at three levels of precision. The loosest is text: cinematography terms like "slow pan left," "crane up" or "orbit around the subject" in the prompt. The next is preset controls, which several commercial tools, including Runway and Kling, offer as sliders or buttons for common moves. The most precise is explicit trajectories from research systems. MotionCtrl (2023) adds separate modules for camera motion and object motion. CameraCtrl (2024) encodes each frame's camera pose as a Plücker embedding and feeds it through a trainable adapter into a pretrained video model. AnimateDiff's MotionLoRAs are small adapters trained for specific moves such as zooms and pans. Several things make precise control hard. Most training videos carry no camera labels, and captions rarely describe camera motion accurately. Datasets with estimated camera poses, such as RealEstate10K, built from real estate videos with poses recovered by structure-from-motion style methods, are narrow in domain and mostly show static scenes. Models also mix up camera motion and subject motion: ask for a pan and the subject may walk instead. Terminology is another trap. A zoom changes focal length, which enlarges the image without parallax. A dolly physically moves the camera, so near objects shift relative to far ones. Users and models often confuse the two. Monocular video also has scale ambiguity, meaning no absolute scale, so a request like "move two meters" has no fixed meaning unless trajectories are normalized. Orbits require inventing unseen sides of objects and keeping them consistent, and long orbits tend to drift. A common misconception is that the model moves a virtual camera through a 3D scene. It generates pixels that match patterns it learned, and camera movement is one of those learned patterns, not an explicit 3D operation.