The field of autonomous driving increasingly demands high-quality annotated training data. In this paper, we propose Panacea, an innovative approach to generate panoramic and controllable videos in driving scenarios, capable of yielding an unlimited numbers of diverse, annotated samples pivotal for autonomous driving advancements. Panacea addresses two critical challenges: 'Consistency' and 'Controllability.' Consistency ensures temporal and cross-view coherence, while Controllability ensures the alignment of generated content with corresponding annotations. Our approach integrates a novel 4D attention and a two-stage generation pipeline to maintain coherence, supplemented by the ControlNet framework for meticulous control by the Bird's-Eye-View (BEV) layouts. Extensive qualitative and quantitative evaluations of Panacea on the nuScenes dataset prove its effectiveness in generating high-quality multi-view driving-scene videos. This work notably propels the field of autonomous driving by effectively augmenting the training dataset used for advanced BEV perception techniques.
@article{arxiv.2311.16813,
title = {Panacea: Panoramic and Controllable Video Generation for Autonomous Driving},
author = {Yuqing Wen and Yucheng Zhao and Yingfei Liu and Fan Jia and Yanhui Wang and Chong Luo and Chi Zhang and Tiancai Wang and Xiaoyan Sun and Xiangyu Zhang},
journal= {arXiv preprint arXiv:2311.16813},
year = {2023}
}