  Generative Adversarial Networks (GANs) are machine learning methods that are
used in many important and novel applications. For example, in imaging science,
GANs are effectively utilized in generating image datasets, photographs of
human faces, image and video captioning, image-to-image translation,
text-to-image translation, video prediction, and 3D object generation to name a
few. In this paper, we discuss how GANs can be used to create an artificial
world. More specifically, we discuss how GANs help to describe an image
utilizing image/video captioning methods and how to translate the image to a
new image using image-to-image translation frameworks in a theme we desire. We
articulate how GANs impact creating a customized world.
