Generative Adversarial Network Applications in Creating a Meta-Universe
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 23 Jan 2022.
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11 of 17 references.
References · 11
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2014Deep Learning Face Attributes in the Wildcited by 3,693
- 2017Wasserstein GANcited by 639
- 2015Adversarial Autoencoderscited by 364
- 2017Towards Diverse and Natural Image Descriptions via a Conditional GANcited by 59
- 2021JoJoGAN: One Shot Face Stylizationcited by 49
- 2018Adversarial Inference for Multi-Sentence Video Descriptioncited by 31
- 2021Automatic Generation of Descriptive Titles for Video Clips Using Deep Learningcited by 7
- 2019Towards Generating Stylized Image Captions via Adversarial Trainingcited by 5
- 2020Stereotype-Free Classification of Fictitious Facescited by 4
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