Real-Time Generative AI in Game Texture Rendering : A Systematic Analysis of Visual Enhancement Technologies
DOI:
https://doi.org/10.32628/CSEIT241061112Keywords:
Generative Artificial Intelligence, Real-Time Rendering, Texture Synthesis, Game Development Technologies, Visual Computing OptimizationAbstract
This article examines the integration of generative artificial intelligence (GAI) integration in real-time 3D texture rendering for modern gaming applications, presenting theoretical frameworks and practical implementations. Through a comprehensive analysis of current implementations across multiple game development platforms, the article demonstrates how GAI algorithms can dynamically generate and modify high-fidelity textures during gameplay, resulting in a 47% reduction in manual texture creation time and a 32% improvement in runtime performance compared to traditional methods. The article employs a mixed-methods approach, combining quantitative performance metrics with a qualitative assessment of visual quality and player immersion across 150 procedurally generated environments. The article findings indicate that GAI-driven texture rendering streamlines the development pipeline and enables advanced features such as context-aware texture adaptation and dynamic environmental response systems. Furthermore, the results suggest that this technology significantly enhances player immersion, with 85% of test subjects reporting improved visual consistency and environmental reactivity. This article contributes to the growing body of knowledge in real-time rendering optimization. It provides a foundation for future developments in automated game asset generation while also addressing critical challenges in processing overhead and quality consistency management.
Downloads
References
Karras, T., Laine, S., & Aila, T. (2023). "A Style-Based Generator Architecture for Generative Adversarial Networks," arXiv:2301.13181 [cs.CV]. https://arxiv.org/abs/1812.04948
Isola, P., Zhu, J.Y., Zhou, T., & Efros, A.A. (2023). "Image-to-Image Translation with Conditional Adversarial Networks." The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. https://arxiv.org/abs/1611.07004
Kang, L.,(2023). "TextureGAN: Controlling Deep Image Synthesis with Texture Patches," IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/1706.02823
Gatys, L. A.,(2015). "Texture Synthesis Using Convolutional Neural Networks," Advances in Neural Information Processing Systems 28 (NIPS 2015). https://arxiv.org/abs/1505.07376
Epic Games. (2023). "Physically Based Materials," Unreal Engine 5 Documentation. https://docs.unrealengine.com/5.0/en-US/physically-based-materials-in-unreal-engine/
Unity Technologies. (2023). "High Definition Render Pipeline overview," Unity HDRP Documentation. https://docs.unity3d.com/Packages/[email protected]/manual/index.html
Mozilla Developer Network. (2023). "WebGL best practices," MDN Web Docs. https://developer.mozilla.org/en-US/docs/Web/API/WebGL_API/WebGL_best_practices
Khronos Group. (2023). "Best Practices Validation," Vulkan Documentation. vulkan.lunarg.com/doc/view/latest/linux/best_practices.html
OpenGL Working Group. (2024). "OpenGL ES Version 3.2," Khronos OpenGL ES Registry. https://registry.khronos.org/OpenGL/specs/es/3.2/es_spec_3.2.pdf
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
