Panacea: Panoramic and Controllable Video Generation for Autonomous
  Driving
Yuqing Wen Yucheng Zhao Yingfei Liu Fan Jia Yanhui Wang Chong Luo Chi Zhang Tiancai Wang Xiaoyan Sun Xiangyu Zhang
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.