Publications

@inproceedings{behravan_transcending_2025,

title = {Transcending Dimensions Using Generative AI: Real-Time 3D Model Generation in Augmented Reality},

author = {M. Behravan and M. Haghani and D. Gračanin},

editor = {Chen J.Y.C. and Fragomeni G.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007690904&doi=10.1007%2f978-3-031-93700-2_2&partnerID=40&md5=1c4d643aad88d08cbbc9dd2c02413f10},

doi = {10.1007/978-3-031-93700-2_2},

isbn = {03029743 (ISSN); 978-303193699-9 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15788 LNCS},

pages = {13–32},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Traditional 3D modeling requires technical expertise, specialized software, and time-intensive processes, making it inaccessible for many users. Our research aims to lower these barriers by combining generative AI and augmented reality (AR) into a cohesive system that allows users to easily generate, manipulate, and interact with 3D models in real time, directly within AR environments. Utilizing cutting-edge AI models like Shap-E, we address the complex challenges of transforming 2D images into 3D representations in AR environments. Key challenges such as object isolation, handling intricate backgrounds, and achieving seamless user interaction are tackled through advanced object detection methods, such as Mask R-CNN. Evaluation results from 35 participants reveal an overall System Usability Scale (SUS) score of 69.64, with participants who engaged with AR/VR technologies more frequently rating the system significantly higher, at 80.71. This research is particularly relevant for applications in gaming, education, and AR-based e-commerce, offering intuitive, model creation for users without specialized skills. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {3D Model Generation, 3D modeling, 3D models, 3d-modeling, Augmented Reality, Generative AI, Image-to-3D conversion, Model generation, Object Detection, Object recognition, Objects detection, Real- time, Specialized software, Technical expertise, Three dimensional computer graphics, Usability engineering},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{weid_gendeck_2024,

title = {GenDeck: Towards a HoloDeck with Text-to-3D Model Generation},

author = {M. Weid and N. Khezrian and A. P. Mana and F. Farzinnejad and J. Grubert},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195600251&doi=10.1109%2fVRW62533.2024.00388&partnerID=40&md5=6dab0cc05259fa2dbe0a2b3806e569af},

doi = {10.1109/VRW62533.2024.00388},

isbn = {979-835037449-0 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},

pages = {1188–1189},

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

abstract = {Generative Artificial Intelligence has the potential to substantially transform the way 3D content for Extended Reality applications is produced. Specifically, the development of text-to-3D and image-to-3D generators with increasing visual fidelity and decreasing computational costs is thriving quickly. Within this work, we present GenDeck, a proof-of-concept application to experience text-to-3D model generation inside an immersive Virtual Reality environment. © 2024 IEEE.},

keywords = {3D content, 3D modeling, 3D models, 3d-modeling, Computational costs, Extende Reality, Human computer interaction, Immersive virtual reality, Knowledge Work, Model generation, Proof of concept, Three dimensional computer graphics, Virtual Reality, Visual fidelity},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{yeo_aicrid_2023,

title = {AICRID: AI-Empowered CR For Interior Design},

author = {J. Q. Yeo and Y. Wang and S. Tanary and J. Cheng and M. Lau and A. B. Ng and F. Guan},

editor = {Bruder G. and Olivier A.H. and Cunningham A. and Peng E.Y. and Grubert J. and Williams I.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180375829&doi=10.1109%2fISMAR-Adjunct60411.2023.00184&partnerID=40&md5=b14d89dbd38a4dfe3f85b90800d42e78},

doi = {10.1109/ISMAR-Adjunct60411.2023.00184},

isbn = {979-835032891-2 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct},

pages = {837–841},

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

abstract = {Augmented Reality (AR) technologies have been utilized for interior design for years. Normally 3D furniture models need to be created manually or by scanning with specialized devices and this is usually a costly process. Additionally, users need controllers or hands for manipulating the virtual furniture which may lead to fatigue for long-time usage. Artificial Intelligence (AI) technologies have made it possible to generate 3D models from texts, images or both and show potential to automate interactions through the user's voice. We propose a novel design, AICRID in short, which aims to automate the 3D model generation and to facilitate the interactions for interior design AR by leveraging on AI technologies. Specifically, our design will allow the users to directly generate 3D furniture models with generative AI, enabling them to directly interact with the virtual objects through their voices. © 2023 IEEE.},

keywords = {3D modeling, 3D models, 3d-modeling, Architectural design, Artificial intelligence, Artificial intelligence technologies, Augmented Reality, Augmented reality technology, Interior Design, Interior designs, machine learning, Machine-learning, Model generation, Novel design, Text images, User need, Visualization},

pubstate = {published},

tppubtype = {inproceedings}

}