用户可参考视频复现镜头运动，无需参数或微调。
CamCloneMaster: Enabling Reference-based Camera Control for Video Generation
- 通过参考视频直接复制镜头动作，无需输入参数
- 在图像与视频生成任务中均实现高精度镜头控制
- 自建大规模合成数据集，支持复杂场景学习
镜头控制对生成富有表现力的电影级视频至关重要。现有方法依赖显式的镜头参数序列作为控制条件，用户构建复杂镜头运动时尤为繁琐。为此，我们提出CamCloneMaster框架，使用户能从参考视频中复制镜头运动，无需提供镜头参数或测试时微调。该框架统一支持图像到视频和视频到视频任务的参考式镜头控制。此外，我们构建了大规模合成数据集Camera Clone Dataset，涵盖多样场景、主体与镜头运动。大量实验与用户研究显示，CamCloneMaster在镜头可控性与视觉质量上均优于现有方法。
原文摘要 · Abstract (English)
Camera control is crucial for generating expressive and cinematic videos. Existing methods rely on explicit sequences of camera parameters as control conditions, which can be cumbersome for users to construct, particularly for intricate camera movements. To provide a more intuitive camera control method, we propose CamCloneMaster, a framework that enables users to replicate camera movements from reference videos without requiring camera parameters or test-time fine-tuning. CamCloneMaster seamlessly supports reference-based camera control for both Image-to-Video and Video-to-Video tasks within a unified framework. Furthermore, we present the Camera Clone Dataset, a large-scale synthetic dataset designed for camera clone learning, encompassing diverse scenes, subjects, and camera movements. Extensive experiments and user studies demonstrate that CamCloneMaster outperforms existing methods in terms of both camera controllability and visual quality.
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