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http://dx.doi.org/10.9717/kmms.2020.24.5.736

A Study of an AI-Based Content Source Data Generation Model using Folk Paintings and Genre Paintings  

Yang, Seokhwan (Institute of Image and Cultural Contents, Dongguk University)
Lee, Young-Suk (Institute of Image and Cultural Contents, Dongguk University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.5, 2021 , pp. 736-743 More about this Journal
Abstract
Due to COVID-19, the non-face-to-face content market is growing rapidly. However, most of the non-face-to-face content such as webtoons and web novels are produced based on the traditional culture of other countries, not Korean traditional culture. The biggest cause of this situation is the lack of reference materials for creating based on Korean traditional culture. Therefore, the need for materials on traditional Korean culture that can be used for content creation is emerging. In this paper, we propose a generation model of source data based on traditional folk paintings through the fusion of traditional Korean folk paintings and AI technology. The proposed model secures basic data based on folk tales, analyzes the style and characteristics of folk tales, and converts historical backgrounds and various stories related to folk tales into data. In addition, using the built data, various new stories are created based on AI technology. The proposed model is highly utilized in that it provides a foundation for new creation based on Korean traditional folk painting and AI technology.
Keywords
Korean Traditional Folk Paintings; AI; Content Source Data; Content Generation Model; Automatic Story Generation;
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Times Cited By KSCI : 1  (Citation Analysis)
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