CameraCtrl: Enabling Camera Control for Text-to-Video Generation

TL;DR

CameraCtrl introduces a method for precise camera pose control in text-to-video generation, enhancing narrative expressiveness and customization by training a plug-and-play module on top of existing diffusion models.

Contribution

It presents a novel camera control module that can be integrated into video diffusion models without altering their core, improving controllability and generalization in video synthesis.

Findings

CameraCtrl achieves accurate camera pose control in generated videos.

Training on datasets with diverse camera distributions improves controllability.

The method enhances dynamic and customized video storytelling capabilities.

Abstract

Controllability plays a crucial role in video generation, as it allows users to create and edit content more precisely. Existing models, however, lack 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 video diffusion models. Our approach explores effective camera trajectory parameterization along with a plug-and-play camera pose control module that is trained on top of a video diffusion model, leaving other modules of the base model untouched. Moreover, a comprehensive study on the effect of various training datasets is conducted, suggesting that videos with diverse camera distributions and similar appearance to the base model indeed enhance controllability and generalization. Experimental results demonstrate the effectiveness of CameraCtrl in achieving precise camera control with different video generation models, marking a step forward in the pursuit of dynamic and customized video storytelling from textual and camera pose inputs.