S-Flow GAN
This work provides a method for domain translation to enhance photo-realism in generated images, which is incremental as it builds on existing GAN architectures.
The paper tackles the problem of generating photo-realistic images from semantic label maps and CG edge maps by training a conditional GAN, and it addresses the lack of realism in existing GANs for computer vision tasks by embedding edge maps in an adversarial mode.
Our work offers a new method for domain translation from semantic label maps and Computer Graphic (CG) simulation edge map images to photo-realistic images. 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.