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Step-Video-TI2V Technical Report: A State-of-the-Art Text-Driven Image-to-Video Generation Model

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arxiv 2503.11251 v1 pith:NQBMULFS submitted 2025-03-14 cs.CV cs.CL

Haoyang Huang , Guoqing Ma , Nan Duan , Xing Chen , Changyi Wan , Ranchen Ming , Tianyu Wang , Bo Wang
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Zhiying Lu Aojie Li Xianfang Zeng Xinhao Zhang Gang Yu Yuhe Yin Qiling Wu Wen Sun Kang An Xin Han Deshan Sun Wei Ji Bizhu Huang Brian Li Chenfei Wu Guanzhe Huang Huixin Xiong Jiaxin He Jianchang Wu Jianlong Yuan Jie Wu Jiashuai Liu Junjing Guo Kaijun Tan Liangyu Chen Qiaohui Chen Ran Sun Shanshan Yuan Shengming Yin Sitong Liu Wei Chen Yaqi Dai Yuchu Luo Zheng Ge Zhisheng Guan Xiaoniu Song Yu Zhou Binxing Jiao Jiansheng Chen Jing Li Shuchang Zhou Xiangyu Zhang Yi Xiu Yibo Zhu Heung-Yeung Shum Daxin Jiang
This is my paper
classification cs.CVcs.CL
keywords step-video-ti2vimage-to-videogenerationstate-of-the-arttext-drivenmodelstep-video-ti2v-evaltask
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 7 Pith papers

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