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.

翻译：合成数据已成为自动驾驶领域基于机器学习的感知的重要组成部分。然而，由于sim2real领域偏移，它仍无法完全替代真实数据。本文提出一种方法，利用增强过程和对抗训练的优势，为行人识别任务合成逼真数据。我们的方法采用由对抗损失驱动的注意力机制，学习领域差异并改善sim2real适应。实验证实，所提出的适应方法对此类差异具有鲁棒性，并同时展现出视觉真实性和语义一致性。此外，我们在行人识别任务上评估了数据生成流程，并证明生成的数据与真实领域属性相似。