Due to the advances in deep reinforcement learning and the demand of large training data, virtual-to-real learning has gained lots of attention from computer vision community recently. As state-of-the-art 3D engines can generate photo-realistic images suitable for training deep neural networks, researchers have been gradually applied 3D virtual environment to learn different tasks including autonomous driving, collision avoidance, and image segmentation, to name a few.