  Synthetic data generation is an appealing approach to generate novel traffic
scenarios in autonomous driving. However, deep learning perception algorithms
trained solely on synthetic data encounter serious performance drops when they
are tested on real data. Such performance drops are commonly attributed to the
domain gap between real and synthetic data. Domain adaptation methods that have
been applied to mitigate the aforementioned domain gap achieve visually
appealing results, but usually introduce semantic inconsistencies into the
translated samples. In this work, we propose a novel, unsupervised, end-to-end
domain adaptation network architecture that enables semantically consistent
\textit{sim2real} image transfer. Our method performs content disentanglement
by employing shared content encoder and fixed style code.
