Procedural Content Generation for Game 3D Modeling: The Investigation of AI-Based Approaches for Improving Game Experience

Boning Shi

doi:10.54254/2755-2721/2025.LD28251

Applied and Computational EngineeringOpen access

Procedural Content Generation for Game 3D Modeling: The Investigation of AI-Based Approaches for Improving Game Experience

Research Article

Open Access

Procedural Content Generation for Game 3D Modeling: The Investigation of AI-Based Approaches for Improving Game Experience

Boning Shi ^1*

¹ College of Design, University of California Davis, Davis, America

^*Corresponding author: aboshi@ucdavis.edu

Published on 22 October 2025

ACE Vol.196

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-451-9

ISBN (Online): 978-1-80590-452-6

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Abstract

Recently, artificial intelligence (AI) has shown its advantages in many fields, one of which is the modeling market in game design, as individual needs of each player can be customized by AI. Meanwhile, over the decades, 3D model games have proven to be a powerful tool to attract players to interact with the game environment to get more immersive experiences. Previous researchers have explored the possibilities of machine learning (ML) and Reinforcement Learning (RL) for generating game content and 3D models. To provide high-quality assets, researchers aim to improve game release efficiency by integrating ML and RL into Procedural Content Generation (PCG), allowing automatic creation of text and non-text files like levels, models, and 3D environments. However, challenges remain in algorithm development and creating high-quality 3D models. This article reviews the current state of AI-driven content generation for game 3D assets, discussing techniques, applications, limitations, and challenges, including natural language processing, RL, and ML algorithms. It also highlights future opportunities, such as developing complex models and exploring new AI applications in game design.

Keywords:

Artificial Intelligence, Procedural Content Generation, Reinforcement Learning, Machine Learning, Game 3D Modeling

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References

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References

[2]. Guzdial, M., & Riedl, M. O. (2018). Combinatorial Creativity for Procedural Content Generation via Machine Learning. In AAAI Workshops (pp. 557-564).

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[18]. Lopes, R., & Bidarra, R. (2011). Adaptivity challenges in games and simulations: a survey. IEEE Transactions on Computational Intelligence and AI in Games, 3(2), 85-99.

Cite this article

Shi,B. (2025). Procedural Content Generation for Game 3D Modeling: The Investigation of AI-Based Approaches for Improving Game Experience. Applied and Computational Engineering,196,61-67.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-451-9(Print) / 978-1-80590-452-6(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 12 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.196

ISSN: 2755-2721(Print) / 2755-273X(Online)

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