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

2024-04-02

arXiv.org

AI Summary

Existing text-to-video generation models lack explicit camera pose modeling, limiting cinematic narrative expression. To address this, we propose a plug-and-play camera pose control module that requires no architectural modification to the underlying video diffusion model. We introduce an efficient trajectory parameterization scheme enabling fine-grained, physically plausible camera motion modeling. The module is trained end-to-end on a diverse, multi-source dataset comprising both real-world and synthetic camera motion trajectories. Extensive evaluation across mainstream video diffusion models—including SVD and AnimateDiff—demonstrates significant improvements in camera control accuracy and dynamic narrative controllability. Crucially, the method preserves generation fidelity while achieving cross-model generalization. Our approach introduces editable, interpretable cinematic language control into video generation, enabling principled composition and motion design without compromising visual quality or model compatibility.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Lack of camera pose control in video generation models.
• Need for precise camera trajectory parameterization in video diffusion models.
• Enhancing controllability and generalization with diverse camera distributions.

Innovation

• CameraCtrl enables precise camera pose control.
• Plug-and-play module enhances video diffusion models.
• Diverse training datasets improve controllability and generalization.