• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.137-144
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• 2024

In this study, we aim to devise an effective generative AI education direction for those learning game development. In the past, artificial intelligence technology was used to create game content, but with the emergence and rapid development of generative AI, its role has expanded to a tool for game development. This is changing the entire game development process. However, these developments brought not only opportunities but also anxiety to those in demand for education. This class is designed to relieve these anxieties, allow educational consumers to create part of the game development process using generative AI rather than traditional game development methods, and change their perception of generative AI through this experience. A post-training survey was conducted to explore perceptions of generative AI and capture the skills needed to use generative AI smoothly and demand for additional training areas. Through this, we propose a method for effectively teaching generative AI technology and suggest implications for the future direction of generative AI education.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.241-248
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• 2023

GAN (Generative Adversarial Networks) creates highly sophisticated counterfeit products by learning real images or text and inferring commonalities. Therefore, it can be useful in fields that require the creation of large-scale images or graphics. In this paper, we implement GAN-based game character creation AI that can dramatically reduce illustration design work costs by providing expansion and automation of game character image creation. This is very efficient in game development as it allows mass production of various character images at low cost.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.90-95
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• 2023

With the rapid development of deep learning and artificial intelligence, generative models have achieved remarkable success in the field of image generation. By combining the stable diffusion method with Web UI technology, a novel solution is provided for the application of AI painting generation. The application prospects of this technology are very broad and can be applied to multiple fields, such as digital art, concept design, game development, and more. Furthermore, the platform based on Web UI facilitates user operations, making the technology more easily applicable to practical scenarios. This paper introduces the basic principles of Stable Diffusion Web UI technology. This technique utilizes the stability of diffusion processes to improve the output quality of generative models. By gradually introducing noise during the generation process, the model can generate smoother and more coherent images. Additionally, the analysis of different model types and applications within Stable Diffusion Web UI provides creators with a more comprehensive understanding, offering valuable insights for fields such as artistic creation and design.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.23-36
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• 2024

Interest in virtual humans continues to increase due to the development of generative AI, extended reality, computer graphics technology, and the spread of a converged metaverse that goes beyond the boundaries between reality and virtuality. Despite the negative public opinion that virtual humans were just temporary form of entertainment event in the early days of their emergence, the reason they are showing continuous growth is due to the unique characteristics of virtual humans and the expansion of diverse usage from technological advancements. The production of video content using virtual humans is becoming vigorously active, but currently there is limitation and no exact process for the technology to apply virtual humans to video content for it to be produced accordingly to the characteristics or situations of virtual humans. In this study, we investigated the characteristics of virtual human production technology methods & processes, and identifying the impact of each production technology on the production environment through examples of virtual human content applied to domestic and international video contents. In conclusion, by proposing an appropriate production method for each content, we hope to develop and assist production practitioners so they can effectively use virtual humans in video content production.