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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/2506.09113 |
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| _version_ | 1866916814268661760 |
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| author | Gao, Yu Guo, Haoyuan Hoang, Tuyen Huang, Weilin Jiang, Lu Kong, Fangyuan Li, Huixia Li, Jiashi Li, Liang Li, Xiaojie Li, Xunsong Li, Yifu Lin, Shanchuan Lin, Zhijie Liu, Jiawei Liu, Shu Nie, Xiaonan Qing, Zhiwu Ren, Yuxi Sun, Li Tian, Zhi Wang, Rui Wang, Sen Wei, Guoqiang Wu, Guohong Wu, Jie Xia, Ruiqi Xiao, Fei Xiao, Xuefeng Yan, Jiangqiao Yang, Ceyuan Yang, Jianchao Yang, Runkai Yang, Tao Yang, Yihang Ye, Zilyu Zeng, Xuejiao Zeng, Yan Zhang, Heng Zhao, Yang Zheng, Xiaozheng Zhu, Peihao Zou, Jiaxin Zuo, Feilong |
| author_facet | Gao, Yu Guo, Haoyuan Hoang, Tuyen Huang, Weilin Jiang, Lu Kong, Fangyuan Li, Huixia Li, Jiashi Li, Liang Li, Xiaojie Li, Xunsong Li, Yifu Lin, Shanchuan Lin, Zhijie Liu, Jiawei Liu, Shu Nie, Xiaonan Qing, Zhiwu Ren, Yuxi Sun, Li Tian, Zhi Wang, Rui Wang, Sen Wei, Guoqiang Wu, Guohong Wu, Jie Xia, Ruiqi Xiao, Fei Xiao, Xuefeng Yan, Jiangqiao Yang, Ceyuan Yang, Jianchao Yang, Runkai Yang, Tao Yang, Yihang Ye, Zilyu Zeng, Xuejiao Zeng, Yan Zhang, Heng Zhao, Yang Zheng, Xiaozheng Zhu, Peihao Zou, Jiaxin Zuo, Feilong |
| contents | Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously balancing prompt following, motion plausibility, and visual quality. In this report, we introduce Seedance 1.0, a high-performance and inference-efficient video foundation generation model that integrates several core technical improvements: (i) multi-source data curation augmented with precision and meaningful video captioning, enabling comprehensive learning across diverse scenarios; (ii) an efficient architecture design with proposed training paradigm, which allows for natively supporting multi-shot generation and jointly learning of both text-to-video and image-to-video tasks. (iii) carefully-optimized post-training approaches leveraging fine-grained supervised fine-tuning, and video-specific RLHF with multi-dimensional reward mechanisms for comprehensive performance improvements; (iv) excellent model acceleration achieving ~10x inference speedup through multi-stage distillation strategies and system-level optimizations. Seedance 1.0 can generate a 5-second video at 1080p resolution only with 41.4 seconds (NVIDIA-L20). Compared to state-of-the-art video generation models, Seedance 1.0 stands out with high-quality and fast video generation having superior spatiotemporal fluidity with structural stability, precise instruction adherence in complex multi-subject contexts, native multi-shot narrative coherence with consistent subject representation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seedance 1.0: Exploring the Boundaries of Video Generation Models Gao, Yu Guo, Haoyuan Hoang, Tuyen Huang, Weilin Jiang, Lu Kong, Fangyuan Li, Huixia Li, Jiashi Li, Liang Li, Xiaojie Li, Xunsong Li, Yifu Lin, Shanchuan Lin, Zhijie Liu, Jiawei Liu, Shu Nie, Xiaonan Qing, Zhiwu Ren, Yuxi Sun, Li Tian, Zhi Wang, Rui Wang, Sen Wei, Guoqiang Wu, Guohong Wu, Jie Xia, Ruiqi Xiao, Fei Xiao, Xuefeng Yan, Jiangqiao Yang, Ceyuan Yang, Jianchao Yang, Runkai Yang, Tao Yang, Yihang Ye, Zilyu Zeng, Xuejiao Zeng, Yan Zhang, Heng Zhao, Yang Zheng, Xiaozheng Zhu, Peihao Zou, Jiaxin Zuo, Feilong Computer Vision and Pattern Recognition Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously balancing prompt following, motion plausibility, and visual quality. In this report, we introduce Seedance 1.0, a high-performance and inference-efficient video foundation generation model that integrates several core technical improvements: (i) multi-source data curation augmented with precision and meaningful video captioning, enabling comprehensive learning across diverse scenarios; (ii) an efficient architecture design with proposed training paradigm, which allows for natively supporting multi-shot generation and jointly learning of both text-to-video and image-to-video tasks. (iii) carefully-optimized post-training approaches leveraging fine-grained supervised fine-tuning, and video-specific RLHF with multi-dimensional reward mechanisms for comprehensive performance improvements; (iv) excellent model acceleration achieving ~10x inference speedup through multi-stage distillation strategies and system-level optimizations. Seedance 1.0 can generate a 5-second video at 1080p resolution only with 41.4 seconds (NVIDIA-L20). Compared to state-of-the-art video generation models, Seedance 1.0 stands out with high-quality and fast video generation having superior spatiotemporal fluidity with structural stability, precise instruction adherence in complex multi-subject contexts, native multi-shot narrative coherence with consistent subject representation. |
| title | Seedance 1.0: Exploring the Boundaries of Video Generation Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.09113 |