Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)

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Research on the Application of AI Generative Models in Games

Authors
Zelun Yang1, *
1Faculty of Science and Technology, Beijing Normal-Hong Kong Baptist University, Zhuhai, 519087, China
*Corresponding author. Email: t330026192@mail.uic.edu.cn
Corresponding Author
Zelun Yang
Available Online 31 August 2025.
DOI
10.2991/978-94-6463-823-3_98How to use a DOI?
Keywords
AI Generative models; Game; Generative Adversarial Networks; Variational Autoencoders; Transformer-based models
Abstract

This paper explores the application of generative AI in game development and design. As artificial intelligence technology undergoes ongoing progress, generative AI has gradually become a focal point of research in game development and design. In the gaming domain, generative AI holds significant importance as it not only enhances development efficiency by automating repetitive tasks and accelerating content creation but also provides players with more diverse and enriched gaming experiences through dynamic and personalized content generation. The paper begins by presenting the core principles of generative AI and explores its potential applications within the gaming industry. Subsequently, it conducts an in-depth analysis of three primary generative models: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models, accompanied by practical application case studies demonstrating their implementation in gaming. Additionally, the paper discusses current limitations of these technologies, such as high computational resource requirements and issues with generated content failing to meet expectations, while offering perspectives on potential future developmental directions.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
Series
Advances in Computer Science Research
Publication Date
31 August 2025
ISBN
978-94-6463-823-3
ISSN
2352-538X
DOI
10.2991/978-94-6463-823-3_98How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Zelun Yang
PY  - 2025
DA  - 2025/08/31
TI  - Research on the Application of AI Generative Models in Games
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 1004
EP  - 1015
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_98
DO  - 10.2991/978-94-6463-823-3_98
ID  - Yang2025
ER  -