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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.16869 |
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| _version_ | 1866911383407296512 |
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| author | Ma, Yue Feng, Kunyu Hu, Zhongyuan Wang, Xinyu Wang, Yucheng Zheng, Mingzhe Wang, Bingyuan Wang, Qinghe He, Xuanhua Wang, Hongfa Zhu, Chenyang Liu, Hongyu He, Yingqing Wang, Zeyu Li, Zhifeng Li, Xiu Han, Sirui Guo, Yike Liu, Wei Xu, Dan Zhang, Linfeng Chen, Qifeng |
| author_facet | Ma, Yue Feng, Kunyu Hu, Zhongyuan Wang, Xinyu Wang, Yucheng Zheng, Mingzhe Wang, Bingyuan Wang, Qinghe He, Xuanhua Wang, Hongfa Zhu, Chenyang Liu, Hongyu He, Yingqing Wang, Zeyu Li, Zhifeng Li, Xiu Han, Sirui Guo, Yike Liu, Wei Xu, Dan Zhang, Linfeng Chen, Qifeng |
| contents | With the rapid development of AI-generated content (AIGC), video generation has emerged as one of its most dynamic and impactful subfields. In particular, the advancement of video generation foundation models has led to growing demand for controllable video generation methods that can more accurately reflect user intent. Most existing foundation models are designed for text-to-video generation, where text prompts alone are often insufficient to express complex, multi-modal, and fine-grained user requirements. This limitation makes it challenging for users to generate videos with precise control using current models. To address this issue, recent research has explored the integration of additional non-textual conditions, such as camera motion, depth maps, and human pose, to extend pretrained video generation models and enable more controllable video synthesis. These approaches aim to enhance the flexibility and practical applicability of AIGC-driven video generation systems. In this survey, we provide a systematic review of controllable video generation, covering both theoretical foundations and recent advances in the field. We begin by introducing the key concepts and commonly used open-source video generation models. We then focus on control mechanisms in video diffusion models, analyzing how different types of conditions can be incorporated into the denoising process to guide generation. Finally, we categorize existing methods based on the types of control signals they leverage, including single-condition generation, multi-condition generation, and universal controllable generation. For a complete list of the literature on controllable video generation reviewed, please visit our curated repository at https://github.com/mayuelala/Awesome-Controllable-Video-Generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16869 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Controllable Video Generation: A Survey Ma, Yue Feng, Kunyu Hu, Zhongyuan Wang, Xinyu Wang, Yucheng Zheng, Mingzhe Wang, Bingyuan Wang, Qinghe He, Xuanhua Wang, Hongfa Zhu, Chenyang Liu, Hongyu He, Yingqing Wang, Zeyu Li, Zhifeng Li, Xiu Han, Sirui Guo, Yike Liu, Wei Xu, Dan Zhang, Linfeng Chen, Qifeng Graphics Computer Vision and Pattern Recognition With the rapid development of AI-generated content (AIGC), video generation has emerged as one of its most dynamic and impactful subfields. In particular, the advancement of video generation foundation models has led to growing demand for controllable video generation methods that can more accurately reflect user intent. Most existing foundation models are designed for text-to-video generation, where text prompts alone are often insufficient to express complex, multi-modal, and fine-grained user requirements. This limitation makes it challenging for users to generate videos with precise control using current models. To address this issue, recent research has explored the integration of additional non-textual conditions, such as camera motion, depth maps, and human pose, to extend pretrained video generation models and enable more controllable video synthesis. These approaches aim to enhance the flexibility and practical applicability of AIGC-driven video generation systems. In this survey, we provide a systematic review of controllable video generation, covering both theoretical foundations and recent advances in the field. We begin by introducing the key concepts and commonly used open-source video generation models. We then focus on control mechanisms in video diffusion models, analyzing how different types of conditions can be incorporated into the denoising process to guide generation. Finally, we categorize existing methods based on the types of control signals they leverage, including single-condition generation, multi-condition generation, and universal controllable generation. For a complete list of the literature on controllable video generation reviewed, please visit our curated repository at https://github.com/mayuelala/Awesome-Controllable-Video-Generation. |
| title | Controllable Video Generation: A Survey |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.16869 |