  Synthetic data became already an essential component of machine
learning-based perception in the field of autonomous driving. Yet it still
cannot replace real data completely due to the sim2real domain shift. In this
work, we propose a method that leverages the advantages of the augmentation
process and adversarial training to synthesize realistic data for the
pedestrian recognition task. Our approach utilizes an attention mechanism
driven by an adversarial loss to learn domain discrepancies and improve
sim2real adaptation. Our experiments confirm that the proposed adaptation
method is robust to such discrepancies and reveals both visual realism and
semantic consistency. Furthermore, we evaluate our data generation pipeline on
the task of pedestrian recognition and demonstrate that generated data resemble
properties of the real domain.
