Our work offers a new method for domain translation from semantic label maps
 and Computer Graphic (CG) simulation edge map images to photo-realistic im-
 ages. We train a Generative Adversarial Network (GAN) in a conditional way to
 generate a photo-realistic version of a given CG scene. Existing architectures of
 GANs still lack the photo-realism capabilities needed to train DNNs for computer
 vision tasks, we address this issue by embedding edge maps, and training it in an
 adversarial mode. We also offer an extension to our model that uses our GAN
 architecture to create visually appealing and temporally coherent videos.
Simulation to real images translation and video generation