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Virtual Try-On With Generative Adversarial Networks: A Taxonomical Survey

Virtual Try-On With Generative Adversarial Networks: A Taxonomical Survey

Andrew Jong, Melody Moh, Teng-Sheng Moh
Copyright: © 2020 |Pages: 25
ISBN13: 9781799844440|ISBN10: 1799844447|ISBN13 Softcover: 9781799852049|EISBN13: 9781799844457
DOI: 10.4018/978-1-7998-4444-0.ch005
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MLA

Jong, Andrew, et al. "Virtual Try-On With Generative Adversarial Networks: A Taxonomical Survey." Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies, edited by Muhammad Sarfraz, IGI Global, 2020, pp. 76-100. https://doi.org/10.4018/978-1-7998-4444-0.ch005

APA

Jong, A., Moh, M., & Moh, T. (2020). Virtual Try-On With Generative Adversarial Networks: A Taxonomical Survey. In M. Sarfraz (Ed.), Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies (pp. 76-100). IGI Global. https://doi.org/10.4018/978-1-7998-4444-0.ch005

Chicago

Jong, Andrew, Melody Moh, and Teng-Sheng Moh. "Virtual Try-On With Generative Adversarial Networks: A Taxonomical Survey." In Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies, edited by Muhammad Sarfraz, 76-100. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-4444-0.ch005

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Abstract

This chapter elaborates on using generative adversarial networks (GAN) for virtual try-on applications. It presents the first comprehensive survey on this topic. Virtual try-on represents a practical application of GANs and pixel translation, which improves on the techniques of virtual try-on prior to these new discoveries. This survey details the importance of virtual try-on systems and the history of virtual try-on; shows how GANs, pixel translation, and perceptual losses have influenced the field; and summarizes the latest research in creating virtual try-on systems. Additionally, the authors present the future directions of research to improve virtual try-on systems by making them usable, faster, more effective. By walking through the steps of virtual try-on from start to finish, the chapter aims to expose readers to key concepts shared by many GAN applications and to give readers a solid foundation to pursue further topics in GANs.

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