Title: S-Flow GAN. Abstract: 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.