I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909516031852544 |
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| author | Feng, Wanquan Liu, Jiawei Tu, Pengqi Qi, Tianhao Sun, Mingzhen Ma, Tianxiang Zhao, Songtao Zhou, Siyu He, Qian |
| author_facet | Feng, Wanquan Liu, Jiawei Tu, Pengqi Qi, Tianhao Sun, Mingzhen Ma, Tianxiang Zhao, Songtao Zhou, Siyu He, Qian |
| contents | Video generation technologies are developing rapidly and have broad potential applications. Among these technologies, camera control is crucial for generating professional-quality videos that accurately meet user expectations. However, existing camera control methods still suffer from several limitations, including control precision and the neglect of the control for subject motion dynamics. In this work, we propose I2VControl-Camera, a novel camera control method that significantly enhances controllability while providing adjustability over the strength of subject motion. To improve control precision, we employ point trajectory in the camera coordinate system instead of only extrinsic matrix information as our control signal. To accurately control and adjust the strength of subject motion, we explicitly model the higher-order components of the video trajectory expansion, not merely the linear terms, and design an operator that effectively represents the motion strength. We use an adapter architecture that is independent of the base model structure. Experiments on static and dynamic scenes show that our framework outperformances previous methods both quantitatively and qualitatively. The project page is: https://wanquanf.github.io/I2VControlCamera . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06525 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength Feng, Wanquan Liu, Jiawei Tu, Pengqi Qi, Tianhao Sun, Mingzhen Ma, Tianxiang Zhao, Songtao Zhou, Siyu He, Qian Computer Vision and Pattern Recognition Artificial Intelligence Video generation technologies are developing rapidly and have broad potential applications. Among these technologies, camera control is crucial for generating professional-quality videos that accurately meet user expectations. However, existing camera control methods still suffer from several limitations, including control precision and the neglect of the control for subject motion dynamics. In this work, we propose I2VControl-Camera, a novel camera control method that significantly enhances controllability while providing adjustability over the strength of subject motion. To improve control precision, we employ point trajectory in the camera coordinate system instead of only extrinsic matrix information as our control signal. To accurately control and adjust the strength of subject motion, we explicitly model the higher-order components of the video trajectory expansion, not merely the linear terms, and design an operator that effectively represents the motion strength. We use an adapter architecture that is independent of the base model structure. Experiments on static and dynamic scenes show that our framework outperformances previous methods both quantitatively and qualitatively. The project page is: https://wanquanf.github.io/I2VControlCamera . |
| title | I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2411.06525 |