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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.11251 |
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| _version_ | 1866916652627525632 |
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| author | Huang, Haoyang Ma, Guoqing Duan, Nan Chen, Xing Wan, Changyi Ming, Ranchen Wang, Tianyu Wang, Bo Lu, Zhiying Li, Aojie Zeng, Xianfang Zhang, Xinhao Yu, Gang Yin, Yuhe Wu, Qiling Sun, Wen An, Kang Han, Xin Sun, Deshan Ji, Wei Huang, Bizhu Li, Brian Wu, Chenfei Huang, Guanzhe Xiong, Huixin He, Jiaxin Wu, Jianchang Yuan, Jianlong Wu, Jie Liu, Jiashuai Guo, Junjing Tan, Kaijun Chen, Liangyu Chen, Qiaohui Sun, Ran Yuan, Shanshan Yin, Shengming Liu, Sitong Chen, Wei Dai, Yaqi Luo, Yuchu Ge, Zheng Guan, Zhisheng Song, Xiaoniu Zhou, Yu Jiao, Binxing Chen, Jiansheng Li, Jing Zhou, Shuchang Zhang, Xiangyu Xiu, Yi Zhu, Yibo Shum, Heung-Yeung Jiang, Daxin |
| author_facet | Huang, Haoyang Ma, Guoqing Duan, Nan Chen, Xing Wan, Changyi Ming, Ranchen Wang, Tianyu Wang, Bo Lu, Zhiying Li, Aojie Zeng, Xianfang Zhang, Xinhao Yu, Gang Yin, Yuhe Wu, Qiling Sun, Wen An, Kang Han, Xin Sun, Deshan Ji, Wei Huang, Bizhu Li, Brian Wu, Chenfei Huang, Guanzhe Xiong, Huixin He, Jiaxin Wu, Jianchang Yuan, Jianlong Wu, Jie Liu, Jiashuai Guo, Junjing Tan, Kaijun Chen, Liangyu Chen, Qiaohui Sun, Ran Yuan, Shanshan Yin, Shengming Liu, Sitong Chen, Wei Dai, Yaqi Luo, Yuchu Ge, Zheng Guan, Zhisheng Song, Xiaoniu Zhou, Yu Jiao, Binxing Chen, Jiansheng Li, Jing Zhou, Shuchang Zhang, Xiangyu Xiu, Yi Zhu, Yibo Shum, Heung-Yeung Jiang, Daxin |
| contents | We present Step-Video-TI2V, a state-of-the-art text-driven image-to-video generation model with 30B parameters, capable of generating videos up to 102 frames based on both text and image inputs. We build Step-Video-TI2V-Eval as a new benchmark for the text-driven image-to-video task and compare Step-Video-TI2V with open-source and commercial TI2V engines using this dataset. Experimental results demonstrate the state-of-the-art performance of Step-Video-TI2V in the image-to-video generation task. Both Step-Video-TI2V and Step-Video-TI2V-Eval are available at https://github.com/stepfun-ai/Step-Video-TI2V. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11251 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Step-Video-TI2V Technical Report: A State-of-the-Art Text-Driven Image-to-Video Generation Model Huang, Haoyang Ma, Guoqing Duan, Nan Chen, Xing Wan, Changyi Ming, Ranchen Wang, Tianyu Wang, Bo Lu, Zhiying Li, Aojie Zeng, Xianfang Zhang, Xinhao Yu, Gang Yin, Yuhe Wu, Qiling Sun, Wen An, Kang Han, Xin Sun, Deshan Ji, Wei Huang, Bizhu Li, Brian Wu, Chenfei Huang, Guanzhe Xiong, Huixin He, Jiaxin Wu, Jianchang Yuan, Jianlong Wu, Jie Liu, Jiashuai Guo, Junjing Tan, Kaijun Chen, Liangyu Chen, Qiaohui Sun, Ran Yuan, Shanshan Yin, Shengming Liu, Sitong Chen, Wei Dai, Yaqi Luo, Yuchu Ge, Zheng Guan, Zhisheng Song, Xiaoniu Zhou, Yu Jiao, Binxing Chen, Jiansheng Li, Jing Zhou, Shuchang Zhang, Xiangyu Xiu, Yi Zhu, Yibo Shum, Heung-Yeung Jiang, Daxin Computer Vision and Pattern Recognition Computation and Language We present Step-Video-TI2V, a state-of-the-art text-driven image-to-video generation model with 30B parameters, capable of generating videos up to 102 frames based on both text and image inputs. We build Step-Video-TI2V-Eval as a new benchmark for the text-driven image-to-video task and compare Step-Video-TI2V with open-source and commercial TI2V engines using this dataset. Experimental results demonstrate the state-of-the-art performance of Step-Video-TI2V in the image-to-video generation task. Both Step-Video-TI2V and Step-Video-TI2V-Eval are available at https://github.com/stepfun-ai/Step-Video-TI2V. |
| title | Step-Video-TI2V Technical Report: A State-of-the-Art Text-Driven Image-to-Video Generation Model |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2503.11251 |