Publications

@inproceedings{kouzelis_synthesizing_2023,

title = {Synthesizing Play-Ready VR Scenes with Natural Language Prompts Through GPT API},

author = {L. R. Kouzelis and O. Spantidi},

editor = {Bebis G. and Ghiasi G. and Fang Y. and Sharf A. and Dong Y. and Weaver C. and Leo Z. and LaViola Jr. J.J. and Kohli L.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180626887&doi=10.1007%2f978-3-031-47966-3_2&partnerID=40&md5=d15c3e2f3260e2a68bdca91c29df7bbb},

doi = {10.1007/978-3-031-47966-3_2},

isbn = {03029743 (ISSN); 978-303147965-6 (ISBN)},

year  = {2023},

date = {2023-01-01},

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

volume = {14362},

pages = {15–26},

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

abstract = {In visual computing, 3D scene generation stands as a crucial component, offering applications in various fields such as gaming, virtual reality (VR), and architectural visualization. Creating realistic and versatile virtual environments, however, poses significant challenges. This work presents a novel methodology that leverages the capabilities of a widely adopted large language model (LLM) to address these challenges. Our approach utilizes the GPT API to interpret natural language prompts and generate detailed, VR-ready scenes within Unity3D. Our work is also inherently scalable, since the model accepts any database of 3D objects with minimal prior configuration. The effectiveness of the proposed system is demonstrated through a series of case studies, revealing its potential to generate diverse and functional virtual spaces. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

keywords = {3-d designs, 3D object, 3D scenes, AI-driven 3D Design, Language Model, Natural languages, Novel methodology, Scene Generation, Three dimensional computer graphics, Unity3d, Virtual Reality, Visual computing},

pubstate = {published},

tppubtype = {inproceedings}

}