Generative AI for VR: Creating Physically Realistic Models

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nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]27/09/2025 at 4:04 PM[/if 2224] | [if 1553 equals=””] Volume : 12 [else] Volume : 12[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 14 22

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    Varun Bhilare, Pranita Chaudhary, Aayush Bodkhe, Abhishek Bichare, Shushant Bhat,

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  1. Student, Assistant Professor, Student, Student, Student, Department of Computer Science & Engineering (Artificial Intelligence and Machine Learning), Pimpri Chinchwad College of Engineering, Pune, Department of Computer Science & Engineering (Artificial Intelligence and Machine Learning), Pimpri Chinchwad College of Engineering, Pune, Department of Computer Science & Engineering (Artificial Intelligence and Machine Learning), Pimpri Chinchwad College of Engineering, Pune, Department of Computer Science & Engineering (Artificial Intelligence and Machine Learning), Pimpri Chinchwad College of Engineering, Pune, Department of Computer Science & Engineering (Artificial Intelligence and Machine Learning), Pimpri Chinchwad College of Engineering, Pune, Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra, India, India, India, India, India

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Abstract

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nVirtual Reality has revolutionized the traditional learning system by creating and interactive and engaging environment. However, its ability to show precise real-world experiences is limited due to lack of physical realism. This study investigates the potential of Generative Adversarial Network (GAN) in creating physically realistic 3D models. Proposed system incorporates deep learning techniques along with physics-based constraints to enhance model’s accuracy and usability. To achieve this, experiments were conducted on Shapenet dataset, various preprocessing were done to standardize the point cloud dataset. A multicomponent GAN architecture was used to generate the 3D model. It included spatial transformer as well as a semantic segmentation network. The results demonstrated by the model included good Intersection-over-Union (IoU) score and the dice coefficient over various categories. Comparative evaluation included much improvement in accuracy and realism than conventional method. Moreover, the qualitative analysis showcases clear segmentation and improved interactivity. This highlighted the system’s potential in bridging the gap between the virtual and real-world dynamics. All these findings indicate that the GAN generated models can provide more effective and indulging VR based learning experience. The study concludes that including deep learning along with physics-based constraints offers a promising pathway for advance educational applications and providing deep understanding and engagement among learners.nn

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Keywords: Point cloud, generative AI, virtual reality (VR), physics simulation, GAN, VAE

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Advancements in Robotics ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Advancements in Robotics (joarb)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nVarun Bhilare, Pranita Chaudhary, Aayush Bodkhe, Abhishek Bichare, Shushant Bhat. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Generative AI for VR: Creating Physically Realistic Models[/if 2584]. Journal of Advancements in Robotics. 19/09/2025; 12(03):14-22.

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nVarun Bhilare, Pranita Chaudhary, Aayush Bodkhe, Abhishek Bichare, Shushant Bhat. [if 2584 equals=”][226 striphtml=1][else]Generative AI for VR: Creating Physically Realistic Models[/if 2584]. Journal of Advancements in Robotics. 19/09/2025; 12(03):14-22. Available from: https://journals.stmjournals.com/joarb/article=19/09/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Journal of Advancements in Robotics

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[if 344 not_equal=””]ISSN: 2455-1872[/if 344]

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Volume 12
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 16/05/2025
Accepted 24/06/2025
Published 19/09/2025
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Publication Time 126 Days

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