  Bird's-Eye View (BEV) Perception has received increasing attention in recent
years as it provides a concise and unified spatial representation across views
and benefits a diverse set of downstream driving applications. At the same
time, data-driven simulation for autonomous driving has been a focal point of
recent research but with few approaches that are both fully data-driven and
controllable. Instead of using perception data from real-life scenarios, an
ideal model for simulation would generate realistic street-view images that
align with a given HD map and traffic layout, a task that is critical for
visualizing complex traffic scenarios and developing robust perception models
for autonomous driving. In this paper, we propose BEVGen, a conditional
generative model that synthesizes a set of realistic and spatially consistent
surrounding images that match the BEV layout of a traffic scenario. BEVGen
incorporates a novel cross-view transformation with spatial attention design
which learns the relationship between cameras and map views to ensure their
consistency. We evaluate the proposed model on the challenging NuScenes and
Argoverse 2 datasets. After training, BEVGen can accurately render road and
lane lines, as well as generate traffic scenes with diverse different weather
conditions and times of day.
