RESEARCH PAPER

Seedance 1.0: Exploring the Boundaries of Video Generation Models

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Yu Gao; Haoyuan Guo; Tuyen Hoang; Weilin Huang; Lu Jiang; Fangyuan Kong; Huixia Li; Jiashi Li; Liang Li; Xiaojie Li; Xunsong Li; Yifu Li; Shanchuan Lin; Zhijie Lin; Jiawei Liu; Shu Liu; Xiaonan Nie; Zhiwu Qing; Yuxi Ren; Li Sun; Zhi Tian; Rui Wang; Sen Wang; Guoqiang Wei; Guohong Wu; Jie Wu; Ruiqi Xia; Fei Xiao; Xuefeng Xiao; Jiangqiao Yan; Ceyuan Yang; Jianchao Yang; Runkai Yang; Tao Yang; Yihang Yang; Zilyu Ye; Xuejiao Zeng; Yan Zeng; Heng Zhang; Yang Zhao; Xiaozheng Zheng; Peihao Zhu; Jiaxin Zou; Feilong Zuo

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Components of WAMs
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Category review. Seedance 1.0 provides a general text/image-conditioned video foundation model and causal video VAE. Its media-generation backbone supplies transferable temporal priors rather than a task-specific robot policy, matching the manuscript component/pretraining discussion. Reading evidence

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