Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911988427259904 |
|---|---|
| author | Wen, Yuqing Zhao, Yucheng Liu, Yingfei Huang, Binyuan Jia, Fan Wang, Yanhui Zhang, Chi Wang, Tiancai Sun, Xiaoyan Zhang, Xiangyu |
| author_facet | Wen, Yuqing Zhao, Yucheng Liu, Yingfei Huang, Binyuan Jia, Fan Wang, Yanhui Zhang, Chi Wang, Tiancai Sun, Xiaoyan Zhang, Xiangyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07605 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving Wen, Yuqing Zhao, Yucheng Liu, Yingfei Huang, Binyuan Jia, Fan Wang, Yanhui Zhang, Chi Wang, Tiancai Sun, Xiaoyan Zhang, Xiangyu Computer Vision and Pattern Recognition 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. |
| title | Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.07605 |