Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving

Autor:	Wen, Yuqing, Zhao, Yucheng, Liu, Yingfei, Huang, Binyuan, Jia, Fan, Wang, Yanhui, Zhang, Chi, Wang, Tiancai, Sun, Xiaoyan, Zhang, Xiangyu
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	The field of autonomous driving increasingly demands high-quality annotated video training data. In this paper, we propose Panacea+, a powerful and universally applicable framework for generating video data in driving scenes. Built upon the foundation of our previous work, Panacea, Panacea+ adopts a multi-view appearance noise prior mechanism and a super-resolution module for enhanced consistency and increased resolution. Extensive experiments show that the generated video samples from Panacea+ greatly benefit a wide range of tasks on different datasets, including 3D object tracking, 3D object detection, and lane detection tasks on the nuScenes and Argoverse 2 dataset. These results strongly prove Panacea+ to be a valuable data generation framework for autonomous driving. Comment: Project page: https://panacea-ad.github.io/. arXiv admin note: text overlap with arXiv:2311.16813
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.07605 Zobrazit plný text záznamu View this record from Arxiv