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The first two authors contributed equally.Zhao Yang, Zezhong Qian, Ruohong Yu and Longjun Liu are with the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence,National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University. (e-mail: yangzhao17; zezhongqian; 2233113339@stu.xjtu.edu.cn; liulongjun@xjtu.edu.cn)Xiaofan Li is with the College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China. (e-mail: shalfunnn@gmail.com)Weixiang Xu is with Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. (e-mail: wxxu218@gmail.com)Lingsi Zhu and Gongpeng Zhao are with the University of Science and Technology of China, Anhui 230052, China. (e-mail: ls-zhu24, zgp0531@mail.ustc.edu.cn)
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Ditch the bounding boxes: DualDiff leverages Occupancy Ray-shape Sampling to generate driving scene videos with unprecedented fidelity, outperforming existing methods by a significant margin in both generation quality and downstream task performance.
