CamCloneMaster: Enabling Reference-based Camera Control for Video Generation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916776237858816 |
|---|---|
| author | Luo, Yawen Bai, Jianhong Shi, Xiaoyu Xia, Menghan Wang, Xintao Wan, Pengfei Zhang, Di Gai, Kun Xue, Tianfan |
| author_facet | Luo, Yawen Bai, Jianhong Shi, Xiaoyu Xia, Menghan Wang, Xintao Wan, Pengfei Zhang, Di Gai, Kun Xue, Tianfan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03140 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CamCloneMaster: Enabling Reference-based Camera Control for Video Generation Luo, Yawen Bai, Jianhong Shi, Xiaoyu Xia, Menghan Wang, Xintao Wan, Pengfei Zhang, Di Gai, Kun Xue, Tianfan Computer Vision and Pattern Recognition 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. |
| title | CamCloneMaster: Enabling Reference-based Camera Control for Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.03140 |