Generative Adversarial Network Applications in Creating a Meta-Universe
This work addresses the problem of building customized virtual worlds for applications in entertainment or simulation, but it appears incremental as it applies existing GAN methods to a new domain.
The paper explores using Generative Adversarial Networks (GANs) to create an artificial world, focusing on applications like image/video captioning and image-to-image translation to customize environments.
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.