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.
@article{arxiv.2408.07605,
title = {Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving},
author = {Yuqing Wen and Yucheng Zhao and Yingfei Liu and Binyuan Huang and Fan Jia and Yanhui Wang and Chi Zhang and Tiancai Wang and Xiaoyan Sun and Xiangyu Zhang},
journal= {arXiv preprint arXiv:2408.07605},
year = {2024}
}
Comments
Project page: https://panacea-ad.github.io/. arXiv admin note: text overlap with arXiv:2311.16813