  Despite the recent progress of generative adversarial networks (GANs) at
synthesizing photo-realistic images, producing complex urban scenes remains a
challenging problem. Previous works break down scene generation into two
consecutive phases: unconditional semantic layout synthesis and image synthesis
conditioned on layouts. In this work, we propose to condition layout generation
as well for higher semantic control: given a vector of class proportions, we
generate layouts with matching composition. To this end, we introduce a
conditional framework with novel architecture designs and learning objectives,
which effectively accommodates class proportions to guide the scene generation
process. The proposed architecture also allows partial layout editing with
interesting applications. Thanks to the semantic control, we can produce
layouts close to the real distribution, helping enhance the whole scene
generation process. On different metrics and urban scene benchmarks, our models
outperform existing baselines. Moreover, we demonstrate the merit of our
approach for data augmentation: semantic segmenters trained on real
layout-image pairs along with additional ones generated by our approach
outperform models only trained on real pairs.
