Publications

@inproceedings{gottsacker_rlty2rlty_2024,

title = {rlty2rlty: Transitioning Between Realities with Generative AI},

author = {M. Gottsacker and G. Bruder and G. F. Welch},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195556960&doi=10.1109%2fVRW62533.2024.00374&partnerID=40&md5=c6291f48ce2135a795a0a2d34681b83d},

doi = {10.1109/VRW62533.2024.00374},

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

year  = {2024},

date = {2024-01-01},

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

pages = {1160–1161},

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

abstract = {We present a system for visually transitioning a mixed reality (MR) user between two arbitrary realities (e.g., between two virtual worlds or between the real environment and a virtual world). The system uses artificial intelligence (AI) to generate a 360° video that transforms the user's starting environment to another environment, passing through a liminal space that could help them relax between tasks or prepare them for the ending environment. The video can then be viewed on an MR headset. © 2024 IEEE.},

keywords = {Human computer interaction, Human computer interaction (HCI), Human-centered computing, Interaction paradigm, Interaction paradigms, Interactive computer graphics, Liminal spaces, Mixed / augmented reality, Mixed reality, Real environments, System use, User interfaces, Virtual worlds},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jeong_function-adaptive_2024,

title = {Function-Adaptive Affordance Extraction from 3D Objects Using LLM for Interaction Authoring with Augmented Artifacts},

author = {E. Jeong and H. Kim and S. Park and S. Yoon and J. Ahn and W. Woo},

editor = {Eck U. and Sra M. and Stefanucci J. and Sugimoto M. and Tatzgern M. and Williams I.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214379963&doi=10.1109%2fISMAR-Adjunct64951.2024.00050&partnerID=40&md5=7222e0599a7e2aa0adaea38e4b9e13cc},

doi = {10.1109/ISMAR-Adjunct64951.2024.00050},

isbn = {979-833150691-9 (ISBN)},

year  = {2024},

date = {2024-01-01},

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

pages = {205–208},

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

abstract = {We propose an algorithm that extracts the most suitable affordances, interaction targets, and corresponding coordinates adaptively from 3D models of various artifacts based on their functional context for efficient authoring of XR content with artifacts. Traditionally, authoring AR scenes to convey artifact context required one-to-one manual work. Our approach leverages a Large Language Model (LLM) to extract interaction types, positions, and subjects based on the artifact's name and usage context. This enables templated XR experience creation, replacing repetitive manual labor. Consequently, our system streamlines the XR authoring process, making it more efficient and scalable. © 2024 IEEE.},

keywords = {3D modeling, Applied computing, Art and humanity, Artificial intelligence, Arts and humanities, Augmented Reality, Computer interaction, Computer vision, Computing methodologies, computing methodology, Human computer interaction, Human computer interaction (HCI), Human-centered computing, Humanities computing, Interaction paradigm, Interaction paradigms, Language processing, Mixed / augmented reality, Mixed reality, Modeling languages, Natural Language Processing, Natural language processing systems, Natural languages, Three dimensional computer graphics},

pubstate = {published},

tppubtype = {inproceedings}

}