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Using synthesized images to boost the performance of perception models is along-standing research challenge in computer vision. It becomes more eminent invisual-centric autonomous driving systems with multi-view cameras as somelong-tail scenarios can never be collected. Guided by the BEV segmentationlayouts, the existing generative networks seem to synthesize photo-realisticstreet-view images when evaluated solely on scene-level metrics. However, oncezoom-in, they usually fail to produce accurate foreground and backgrounddetails such as heading. To this end, we propose a two-stage generative method,dubbed BEVControl, that can generate accurate foreground and backgroundcontents. In contrast to segmentation-like input, it also supports sketch styleinput, which is more flexible for humans to edit. In addition, we propose acomprehensive multi-level evaluation protocol to fairly compare the quality ofthe generated scene, foreground object, and background geometry. Our extensiveexperiments show that our BEVControl surpasses the state-of-the-art method,BEVGen, by a significant margin, from 5.89 to 26.80 on foreground segmentationmIoU. In addition, we show that using images generated by BEVControl to trainthe downstream perception model, it achieves on average 1.29 improvement in NDSscore.

BEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout