AHCI RESEARCH GROUP

Publications

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

OUR RESEARCH

Scientific Publications

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2024

Vasic, I.; Fill, H. -G.; Quattrini, R.; Pierdicca, R.

LLM-Aided Museum Guide: Personalized Tours Based on User Preferences Proceedings Article

In: L.T., De Paolis; P., Arpaia; M., Sacco (Ed.): Lect. Notes Comput. Sci., pp. 249–262, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 03029743 (ISSN); 978-303171709-3 (ISBN).

Abstract | Links | BibTeX | Tags: Artificial intelligence techniques, Automated process, Cultural heritages, Extended reality, Language Model, Large language model, large language models, Modeling languages, Museum guide, User's preferences, Virtual environments, Virtual museum, Virtual museums, Virtual tour

@inproceedings{vasic_llm-aided_2024,

title = {LLM-Aided Museum Guide: Personalized Tours Based on User Preferences},

author = {I. Vasic and H. -G. Fill and R. Quattrini and R. Pierdicca},

editor = {De Paolis L.T. and Arpaia P. and Sacco M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205127699&doi=10.1007%2f978-3-031-71710-9_18&partnerID=40&md5=fba73e38a432e0749b8e79197ef85310},

doi = {10.1007/978-3-031-71710-9_18},

isbn = {03029743 (ISSN); 978-303171709-3 (ISBN)},

year  = {2024},

date = {2024-01-01},

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

volume = {15029 LNCS},

pages = {249–262},

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

abstract = {The quick development of generative artificial intelligence (GenAI) techniques is a promising step toward automated processes in the field of cultural heritage (CH). The recent rise of powerful Large Language Models (LLMs) like ChatGPT has made them a commonly utilized tool for a wide range of tasks across various fields. In this paper, we introduce LLMs as a guide in the three-dimensional (3D) panoramic virtual tour of the Civic Art Gallery of Ascoli to enable visitors to express their interest and show them the requested content. The input to our algorithm is a user request in natural language. The processing tasks are performed with the OpenAI’s Generative Pre-trained Transformer (GPT) 4o model. Requests are handled through the OpenAI’s API. We demonstrate all the functionalities within a developed local web-based application. This novel approach is capable of solving the problem of generic guided tours in the museum and offers a solution for the more automatized and personalized ones. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

keywords = {Artificial intelligence techniques, Automated process, Cultural heritages, Extended reality, Language Model, Large language model, large language models, Modeling languages, Museum guide, User's preferences, Virtual environments, Virtual museum, Virtual museums, Virtual tour},

pubstate = {published},

tppubtype = {inproceedings}

}

Bao, Y.; Gao, N.; Weng, D.; Chen, J.; Tian, Z.

MuseGesture: A Framework for Gesture Synthesis by Virtual Agents in VR Museum Guides Proceedings Article

In: U., Eck; M., Sra; J., Stefanucci; M., Sugimoto; M., Tatzgern; I., Williams (Ed.): Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct, pp. 337–338, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833150691-9 (ISBN).

Abstract | Links | BibTeX | Tags: Adversarial machine learning, Embeddings, Gesture Generation, Intelligent Agents, Intelligent systems, Intelligent virtual agents, Language generation, Language Model, Large language model, large language models, Museum guide, Reinforcement Learning, Reinforcement learnings, Robust language understanding, Virtual agent, Virtual Agents, Virtual environments, Virtual reality museum guide, VR Museum Guides

@inproceedings{bao_musegesture_2024,

title = {MuseGesture: A Framework for Gesture Synthesis by Virtual Agents in VR Museum Guides},

author = {Y. Bao and N. Gao and D. Weng and J. Chen and Z. Tian},

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-s2.0-85214385900&doi=10.1109%2fISMAR-Adjunct64951.2024.00079&partnerID=40&md5=e71ffc28e299597557034259aab50641},

doi = {10.1109/ISMAR-Adjunct64951.2024.00079},

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

year  = {2024},

date = {2024-01-01},

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

pages = {337–338},

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

abstract = {This paper presents an innovative framework named MuseGesture, designed to generate contextually adaptive gestures for virtual agents in Virtual Reality (VR) museums. The framework leverages the robust language understanding and generation capabilities of Large Language Models (LLMs) to parse tour narration texts and generate corresponding explanatory gestures. Through reinforcement learning and adversarial skill embeddings, the framework also generates guiding gestures tailored to the virtual museum environment, integrating both gesture types using conditional motion interpolation methods. Experimental results and user studies demonstrate that this approach effectively enables voice-command-controlled virtual guide gestures, offering a novel intelligent guiding system solution that enhances the interactive experience in VR museum environments. © 2024 IEEE.},

keywords = {Adversarial machine learning, Embeddings, Gesture Generation, Intelligent Agents, Intelligent systems, Intelligent virtual agents, Language generation, Language Model, Large language model, large language models, Museum guide, Reinforcement Learning, Reinforcement learnings, Robust language understanding, Virtual agent, Virtual Agents, Virtual environments, Virtual reality museum guide, VR Museum Guides},

pubstate = {published},

tppubtype = {inproceedings}

}