  Image generation has rapidly evolved in recent years. Modern architectures
for adversarial training allow to generate even high resolution images with
remarkable quality. At the same time, more and more effort is dedicated towards
controlling the content of generated images. In this paper, we take one further
step in this direction and propose a conditional generative adversarial network
(GAN) that generates images with a defined number of objects from given
classes. This entails two fundamental abilities (1) being able to generate
high-quality images given a complex constraint and (2) being able to count
object instances per class in a given image. Our proposed model modularly
extends the successful StyleGAN2 architecture with a count-based conditioning
as well as with a regression sub-network to count the number of generated
objects per class during training. In experiments on three different datasets,
we show that the proposed model learns to generate images according to the
given multiple-class count condition even in the presence of complex
backgrounds. In particular, we propose a new dataset, CityCount, which is
derived from the Cityscapes street scenes dataset, to evaluate our approach in
a challenging and practically relevant scenario.
