AHCI RESEARCH GROUP

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Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2025

Behravan, M.; Matković, K.; Gračanin, D.

Generative AI for Context-Aware 3D Object Creation Using Vision-Language Models in Augmented Reality Proceedings Article

In: Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR, pp. 73–81, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833152157-8 (ISBN).

Abstract | Links | BibTeX | Tags: 3D object, 3D Object Generation, Artificial intelligence systems, Augmented Reality, Capture images, Context-Aware, Generative adversarial networks, Generative AI, generative artificial intelligence, Generative model, Language Model, Object creation, Vision language model, vision language models, Visual languages

@inproceedings{behravan_generative_2025,

title = {Generative AI for Context-Aware 3D Object Creation Using Vision-Language Models in Augmented Reality},

author = {M. Behravan and K. Matković and D. Gračanin},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000292700&doi=10.1109%2fAIxVR63409.2025.00018&partnerID=40&md5=b40fa769a6b427918c3fcd86f7c52a75},

doi = {10.1109/AIxVR63409.2025.00018},

isbn = {979-833152157-8 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},

pages = {73–81},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {We present a novel Artificial Intelligence (AI) system that functions as a designer assistant in augmented reality (AR) environments. Leveraging Vision Language Models (VLMs) like LLaVA and advanced text-to-3D generative models, users can capture images of their surroundings with an Augmented Reality (AR) headset. The system analyzes these images to recommend contextually relevant objects that enhance both functionality and visual appeal. The recommended objects are generated as 3D models and seamlessly integrated into the AR environment for interactive use. Our system utilizes open-source AI models running on local systems to enhance data security and reduce operational costs. Key features include context-aware object suggestions, optimal placement guidance, aesthetic matching, and an intuitive user interface for real-time interaction. Evaluations using the COCO 2017 dataset and real-world AR testing demonstrated high accuracy in object detection and contextual fit rating of 4.1 out of 5. By addressing the challenge of providing context-aware object recommendations in AR, our system expands the capabilities of AI applications in this domain. It enables users to create personalized digital spaces efficiently, leveraging AI for contextually relevant suggestions. © 2025 IEEE.},

keywords = {3D object, 3D Object Generation, Artificial intelligence systems, Augmented Reality, Capture images, Context-Aware, Generative adversarial networks, Generative AI, generative artificial intelligence, Generative model, Language Model, Object creation, Vision language model, vision language models, Visual languages},

pubstate = {published},

tppubtype = {inproceedings}

}