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.
authors: Miron Yakov Coscas Yona
hIndex: Miron Yakov 1