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Lookup NU author(s): Dr Lei ShiORCiD
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).
This paper offers a comprehensive review of current developments in artificial intelligence (AI)-based 3D model creation, with an emphasis on techniques utilizing variational autoencoders (VAEs) and generative adversarial networks (GANs). 3DGAN, paired 3D model generation with GAN, conditional GAN, FaceVAE, voxel-based 3D object reconstruction, and 3D-VAE-SDFRaGAN are the six main techniques that are studied in this work. Each method is discussed, highlighting its architectural framework, data representation, and specific approach to generating 3D models. First, the paper introduces basic terms and classical 3D modeling techniques and provides a comparative analysis of them based on their workflow, purpose and field of application. In subsequent chapters, methods for generating 3D models based on the use of GANs and VAEs are reviewed, describing its methodology, experimentation technique, results, and comparison with other methods. The review outlines the strengths and limitations of each approach and their applications in object reconstruction, shape generation, and maintaining model consistency. It concludes by emphasizing how AI-driven methods can advance 3D modeling, underscoring the need for further research to enhance quality, control, and training reliability. The findings show AI’s significant impact on automating complex modeling tasks and enabling new creative opportunities in 3D content development.
Author(s): Adilkhan S, Alimanova M, Shi L, Soltiyeva A
Publication type: Article
Publication status: Published
Journal: Bulletin of Electrical Engineering and Informatics
Year: 2025
Volume: 14
Issue: 6
Pages: 4988-5011
Print publication date: 06/12/2025
Online publication date: 11/12/2025
Acceptance date: 27/09/2025
Date deposited: 08/01/2026
ISSN (print): 2089-3191
ISSN (electronic): 2302-9285
Publisher: Bulletin of Electrical Engineering and Informatics
URL: https://beei.org/index.php/EEI/article/view/10755
DOI: 10.11591/eei.v14i6.10755
Data Access Statement: The authors confirm that the data supporting the findings of this study are available within the article [and/or its supplementary materials]
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