AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Miller, C. H.
Digital Storytelling: A Creator’s Guide to Interactive Entertainment: Volume I, Fifth Edition Book
CRC Press, 2025, ISBN: 978-104034442-2 (ISBN); 978-103285888-3 (ISBN).
Abstract | Links | BibTeX | Tags: Case-studies, Chatbots, Creatives, Digital storytelling, Entertainment, Immersive environment, Interactive documentary, Interactive entertainment, Social media, Use of video, Video-games, Virtual Reality
@book{miller_digital_2025,
title = {Digital Storytelling: A Creator’s Guide to Interactive Entertainment: Volume I, Fifth Edition},
author = {C. H. Miller},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004122515&doi=10.1201%2f9781003520092&partnerID=40&md5=894bfbb310cbd095a54409f9ac5174da},
doi = {10.1201/9781003520092},
isbn = {978-104034442-2 (ISBN); 978-103285888-3 (ISBN)},
year = {2025},
date = {2025-01-01},
volume = {1},
publisher = {CRC Press},
series = {Digital Storytelling: a Creator's Guide to Interactive Entertainment: Volume I, Fifth Edition},
abstract = {Digital Storytelling: A Creator’s Guide to Interactive Entertainment, Volume I, fifth edition delves into the fascinating and groundbreaking stories enabled by interactive digital media, examining both fictional and non-fiction narratives. This fifth edition explores monumental developments, particularly the emergence of generative AI, and highlights exciting projects utilizing this technology. Additionally, it covers social media; interactive documentaries; immersive environments; and innovative uses of video games, chatbots, and virtual reality. Carolyn Handler Miller provides insights into storytelling essentials like character development, plot, structure, dialogue, and emotion, while examining how digital media and interactivity influence these elements. This book also dives into advanced topics, such as narratives using AR, VR, and XR, alongside new forms of immersive media, including large screens, escape rooms, and theme park experiences. With numerous case studies, this edition illustrates the creative possibilities of digital storytelling and its applications beyond entertainment, such as education, training, information, and promotion. Interviews with industry leaders further enhance the understanding of this evolving universe, making it a valuable resource for both professionals and enthusiasts. Key Features: • This book includes up-to-the-minute developments in digital storytelling. • It offers case studies of noteworthy examples of digital storytelling. • It includes a glossary clearly defining new or difficult terms. • Each chapter opens with several thought-provoking questions about the chapter’s topic. • Each chapter concludes with several creative and engaging exercises to promote the reader’s understanding of the chapter’s topic. © 2025 Carolyn Handler Miller.},
keywords = {Case-studies, Chatbots, Creatives, Digital storytelling, Entertainment, Immersive environment, Interactive documentary, Interactive entertainment, Social media, Use of video, Video-games, Virtual Reality},
pubstate = {published},
tppubtype = {book}
}
Digital Storytelling: A Creator’s Guide to Interactive Entertainment, Volume I, fifth edition delves into the fascinating and groundbreaking stories enabled by interactive digital media, examining both fictional and non-fiction narratives. This fifth edition explores monumental developments, particularly the emergence of generative AI, and highlights exciting projects utilizing this technology. Additionally, it covers social media; interactive documentaries; immersive environments; and innovative uses of video games, chatbots, and virtual reality. Carolyn Handler Miller provides insights into storytelling essentials like character development, plot, structure, dialogue, and emotion, while examining how digital media and interactivity influence these elements. This book also dives into advanced topics, such as narratives using AR, VR, and XR, alongside new forms of immersive media, including large screens, escape rooms, and theme park experiences. With numerous case studies, this edition illustrates the creative possibilities of digital storytelling and its applications beyond entertainment, such as education, training, information, and promotion. Interviews with industry leaders further enhance the understanding of this evolving universe, making it a valuable resource for both professionals and enthusiasts. Key Features: • This book includes up-to-the-minute developments in digital storytelling. • It offers case studies of noteworthy examples of digital storytelling. • It includes a glossary clearly defining new or difficult terms. • Each chapter opens with several thought-provoking questions about the chapter’s topic. • Each chapter concludes with several creative and engaging exercises to promote the reader’s understanding of the chapter’s topic. © 2025 Carolyn Handler Miller.
Graziano, M.; Cante, L. Colucci; Martino, B. Di
Deploying Large Language Model on Cloud-Edge Architectures: A Case Study for Conversational Historical Characters Book Section
In: Lecture Notes on Data Engineering and Communications Technologies, vol. 250, pp. 196–205, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 23674512 (ISSN).
Abstract | Links | BibTeX | Tags: Agent based, Augmented Reality, Case-studies, Chatbots, Cloud computing architecture, Conversational Agents, EDGE architectures, Historical characters, Language Model, Modeling languages, Real time performance, WEB application, Web applications, Work analysis
@incollection{graziano_deploying_2025,
title = {Deploying Large Language Model on Cloud-Edge Architectures: A Case Study for Conversational Historical Characters},
author = {M. Graziano and L. Colucci Cante and B. Di Martino},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002995405&doi=10.1007%2f978-3-031-87778-0_19&partnerID=40&md5=c54e9ce66901050a05de68602e4a8266},
doi = {10.1007/978-3-031-87778-0_19},
isbn = {23674512 (ISSN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lecture Notes on Data Engineering and Communications Technologies},
volume = {250},
pages = {196–205},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This work analyzes the deployment of conversational agents based on large language models (LLMs) in cloud-edge architectures, placing emphasis on scalability, efficiency and real-time performance. Through a case study, we present a web application that allows users to interact with an augmented reality avatar that impersonates a historical character. The agent, powered by an LLM delivers immersive and contextually coherent dialogues. We discuss the solutions adopted to manage latency and distribute the computational load between the cloud, which takes care of language processing, and the edge nodes, ensuring a smooth user experience. The results obtained demonstrate how accurate design can optimize the use of LLMs in distributed environments, offering advanced and high-performance interactions even in applications with high reactivity and customization requirements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Agent based, Augmented Reality, Case-studies, Chatbots, Cloud computing architecture, Conversational Agents, EDGE architectures, Historical characters, Language Model, Modeling languages, Real time performance, WEB application, Web applications, Work analysis},
pubstate = {published},
tppubtype = {incollection}
}
This work analyzes the deployment of conversational agents based on large language models (LLMs) in cloud-edge architectures, placing emphasis on scalability, efficiency and real-time performance. Through a case study, we present a web application that allows users to interact with an augmented reality avatar that impersonates a historical character. The agent, powered by an LLM delivers immersive and contextually coherent dialogues. We discuss the solutions adopted to manage latency and distribute the computational load between the cloud, which takes care of language processing, and the edge nodes, ensuring a smooth user experience. The results obtained demonstrate how accurate design can optimize the use of LLMs in distributed environments, offering advanced and high-performance interactions even in applications with high reactivity and customization requirements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Mendoza, A. P.; Quiroga, K. J. Barrios; Celis, S. D. Solano; M., C. G. Quintero
NAIA: A Multi-Technology Virtual Assistant for Boosting Academic Environments—A Case Study Journal Article
In: IEEE Access, vol. 13, pp. 141461–141483, 2025, ISSN: 21693536 (ISSN), (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Abstract | Links | BibTeX | Tags: Academic environment, Artificial intelligence, Case-studies, Computational Linguistics, Computer vision, Digital avatar, Digital avatars, Efficiency, Human computer interaction, Human-AI Interaction, Interactive computer graphics, Language Model, Large language model, large language model (LLM), Learning systems, Natural language processing systems, Personal digital assistants, Personnel training, Population statistics, Speech communication, Speech processing, Speech to text, speech to text (STT), Text to speech, text to speech (TTS), user experience, User interfaces, Virtual assistant, Virtual assistants, Virtual Reality
@article{mendoza_naia_2025,
title = {NAIA: A Multi-Technology Virtual Assistant for Boosting Academic Environments—A Case Study},
author = {A. P. Mendoza and K. J. Barrios Quiroga and S. D. Solano Celis and C. G. Quintero M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105013598763&doi=10.1109%2FACCESS.2025.3597565&partnerID=40&md5=7ad6b037cfedb943fc026642c4854284},
doi = {10.1109/ACCESS.2025.3597565},
issn = {21693536 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Access},
volume = {13},
pages = {141461–141483},
abstract = {Virtual assistants have become essential tools for improving productivity and efficiency in various domains. This paper presents NAIA (Nimble Artificial Intelligence Assistant), an advanced multi-role and multi-task virtual assistant enhanced with artificial intelligence, designed to serve a university community case study. The system integrates AI technologies including Large Language Models (LLM), Computer Vision, and voice processing to create an immersive and efficient interaction through animated digital avatars. NAIA features five specialized roles: researcher, receptionist, personal skills trainer, personal assistant, and university guide, each equipped with specific capabilities to support different aspects of academic life. The system’s Computer Vision capabilities enable it to comment on users’ physical appearance and environment, enriching the interaction. Through natural language processing and voice interaction, NAIA aims to improve productivity and efficiency within the university environment while providing personalized assistance through a ubiquitous platform accessible across multiple devices. NAIA is evaluated through a user experience survey involving 30 participants with different demographic characteristics, this is the most accepted way by the community to evaluate this type of solution. Participants give their feedback after using one role of NAIA after using it for 30 minutes. The experiment showed that 90% of the participants considered NAIA-assisted tasks of higher quality and, on average, NAIA has a score of 4.27 out of 5 on user satisfaction. Participants particularly appreciated the assistant’s visual recognition, natural conversation flow, and user interaction capabilities. Results demonstrate NAIA’s capabilities and effectiveness across the five roles. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Academic environment, Artificial intelligence, Case-studies, Computational Linguistics, Computer vision, Digital avatar, Digital avatars, Efficiency, Human computer interaction, Human-AI Interaction, Interactive computer graphics, Language Model, Large language model, large language model (LLM), Learning systems, Natural language processing systems, Personal digital assistants, Personnel training, Population statistics, Speech communication, Speech processing, Speech to text, speech to text (STT), Text to speech, text to speech (TTS), user experience, User interfaces, Virtual assistant, Virtual assistants, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Virtual assistants have become essential tools for improving productivity and efficiency in various domains. This paper presents NAIA (Nimble Artificial Intelligence Assistant), an advanced multi-role and multi-task virtual assistant enhanced with artificial intelligence, designed to serve a university community case study. The system integrates AI technologies including Large Language Models (LLM), Computer Vision, and voice processing to create an immersive and efficient interaction through animated digital avatars. NAIA features five specialized roles: researcher, receptionist, personal skills trainer, personal assistant, and university guide, each equipped with specific capabilities to support different aspects of academic life. The system’s Computer Vision capabilities enable it to comment on users’ physical appearance and environment, enriching the interaction. Through natural language processing and voice interaction, NAIA aims to improve productivity and efficiency within the university environment while providing personalized assistance through a ubiquitous platform accessible across multiple devices. NAIA is evaluated through a user experience survey involving 30 participants with different demographic characteristics, this is the most accepted way by the community to evaluate this type of solution. Participants give their feedback after using one role of NAIA after using it for 30 minutes. The experiment showed that 90% of the participants considered NAIA-assisted tasks of higher quality and, on average, NAIA has a score of 4.27 out of 5 on user satisfaction. Participants particularly appreciated the assistant’s visual recognition, natural conversation flow, and user interaction capabilities. Results demonstrate NAIA’s capabilities and effectiveness across the five roles. © 2025 Elsevier B.V., All rights reserved.
Paterakis, I.; Manoudaki, N.
Osmosis: Generative AI and XR for the real-time transformation of urban architectural environments Journal Article
In: International Journal of Architectural Computing, 2025, ISSN: 14780771 (ISSN), (Publisher: SAGE Publications Inc.).
Abstract | Links | BibTeX | Tags: Architectural design, Architectural environment, Artificial intelligence, Biodigital design, Case-studies, Computational architecture, Computer architecture, Extended reality, generative artificial intelligence, Immersive, Immersive environment, immersive environments, Natural language processing systems, Real- time, Urban environments, urban planning
@article{paterakis_osmosis_2025,
title = {Osmosis: Generative AI and XR for the real-time transformation of urban architectural environments},
author = {I. Paterakis and N. Manoudaki},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105014516125&doi=10.1177%2F14780771251356526&partnerID=40&md5=4bbcb09440d91899cb7d2d5d0c852507},
doi = {10.1177/14780771251356526},
issn = {14780771 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Architectural Computing},
abstract = {This work contributes to the evolving discourse on biodigital architecture by examining how generative artificial intelligence (AI) and extended reality (XR) systems can be combined to create immersive urban environments. Focusing on the case study of “Osmosis”, a series of large-scale public installations, this work proposes a methodological framework for real-time architectural composition in XR using diffusion models and interaction. The project reframes the architectural façade as a semi permeable membrane, through which digital content diffuses in response to environmental and user inputs. By integrating natural language prompts, multimodal input, and AI-generated visual synthesis with projection mapping, Osmosis advances a vision for urban architecture that is interactive, data-driven, and sensorially rich. The work explores new design territories where stochastic form-making and real-time responsiveness intersect, and positions AI as an augmentation of architectural creativity rather than its replacement. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: SAGE Publications Inc.},
keywords = {Architectural design, Architectural environment, Artificial intelligence, Biodigital design, Case-studies, Computational architecture, Computer architecture, Extended reality, generative artificial intelligence, Immersive, Immersive environment, immersive environments, Natural language processing systems, Real- time, Urban environments, urban planning},
pubstate = {published},
tppubtype = {article}
}
This work contributes to the evolving discourse on biodigital architecture by examining how generative artificial intelligence (AI) and extended reality (XR) systems can be combined to create immersive urban environments. Focusing on the case study of “Osmosis”, a series of large-scale public installations, this work proposes a methodological framework for real-time architectural composition in XR using diffusion models and interaction. The project reframes the architectural façade as a semi permeable membrane, through which digital content diffuses in response to environmental and user inputs. By integrating natural language prompts, multimodal input, and AI-generated visual synthesis with projection mapping, Osmosis advances a vision for urban architecture that is interactive, data-driven, and sensorially rich. The work explores new design territories where stochastic form-making and real-time responsiveness intersect, and positions AI as an augmentation of architectural creativity rather than its replacement. © 2025 Elsevier B.V., All rights reserved.
Alex, G.
Leveraging Large Language Models for Automated XR Instructional Content Generation Proceedings Article
In: Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331585341 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Authoring Tool, Case-studies, Engineering education, Extended reality, IEEE Standards, Language Model, Large language model, Learning systems, Ontology, Ontology's, Simple++
@inproceedings{alex_leveraging_2025,
title = {Leveraging Large Language Models for Automated XR Instructional Content Generation},
author = {G. Alex},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105015398440&doi=10.1109%2FICE%2FITMC65658.2025.11106622&partnerID=40&md5=c125d3b7e58cfff4c24a9b15bb615912},
doi = {10.1109/ICE/ITMC65658.2025.11106622},
isbn = {9798331585341 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This paper presents a study in which authors examine the potential of leveraging large language models to generate instructional content for eXtended Reality environments. Considering the IEEE ARLEM standard as a framework for structuring data, it could be integrated and interpreted by existing authoring tools. In terms of methods, authors have adopted an exploratory approach in testing various strategies. A case study focusing on the use of an eXtended Reality authoring tool for teaching operating procedures is presented. Finally, this exploratory work shows that while simple prompts can produce scenarios with satisfactory quality, imposing a structured schema through more complex prompts leads to less reliable outcomes. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Artificial intelligence, Authoring Tool, Case-studies, Engineering education, Extended reality, IEEE Standards, Language Model, Large language model, Learning systems, Ontology, Ontology's, Simple++},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a study in which authors examine the potential of leveraging large language models to generate instructional content for eXtended Reality environments. Considering the IEEE ARLEM standard as a framework for structuring data, it could be integrated and interpreted by existing authoring tools. In terms of methods, authors have adopted an exploratory approach in testing various strategies. A case study focusing on the use of an eXtended Reality authoring tool for teaching operating procedures is presented. Finally, this exploratory work shows that while simple prompts can produce scenarios with satisfactory quality, imposing a structured schema through more complex prompts leads to less reliable outcomes. © 2025 Elsevier B.V., All rights reserved.
2024
Amato, N.; Carolis, B. De; Gioia, F.; Venezia, M. N.; Palestra, G.; Loglisci, C.
Can an AI-driven VTuber engage People? The KawAIi Case Study Proceedings Article
In: A., Soto; E., Zangerle (Ed.): CEUR Workshop Proc., CEUR-WS, 2024, ISBN: 16130073 (ISSN).
Abstract | Links | BibTeX | Tags: 3D Avatars, Case-studies, Conversational Agents, Facial Expressions, Language Model, Live streaming, LLM, LLMs, Real- time, Three dimensional computer graphics, Virtual agent, Virtual Reality, YouTube
@inproceedings{amato_can_2024,
title = {Can an AI-driven VTuber engage People? The KawAIi Case Study},
author = {N. Amato and B. De Carolis and F. Gioia and M. N. Venezia and G. Palestra and C. Loglisci},
editor = {Soto A. and Zangerle E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190754935&partnerID=40&md5=bd76d56b13e328027aa1b458849cf73f},
isbn = {16130073 (ISSN)},
year = {2024},
date = {2024-01-01},
booktitle = {CEUR Workshop Proc.},
volume = {3660},
publisher = {CEUR-WS},
abstract = {Live streaming has become increasingly popular, with most streamers presenting their real-life appearance. However, Virtual YouTubers (VTubers), virtual 2D or 3D avatars that are voiced by humans, are emerging as live streamers and attracting a growing viewership. This paper presents the development of a conversational agent, named KawAIi, embodied in a 2D character that, while accurately and promptly responding to user requests, provides an entertaining experience in streaming chat platforms such as YouTube while providing adequate real-time support. The agent relies on the Vicuna 7B GPTQ 4-bit Large Language Model (LLM). In addition, KawAIi uses a BERT-based model for analyzing the sentence generated by the model in terms of conveyed emotion and shows self-emotion awareness through facial expressions. Tested with users, the system has demonstrated a good ability to handle the interaction with the user while maintaining a pleasant user experience. In particular, KawAIi has been evaluated positively in terms of engagement and competence on various topics. The results show the potential of this technology to enrich interactivity in streaming platforms and offer a promising model for future online assistance contexts. © 2024 Copyright for this paper by its authors.},
keywords = {3D Avatars, Case-studies, Conversational Agents, Facial Expressions, Language Model, Live streaming, LLM, LLMs, Real- time, Three dimensional computer graphics, Virtual agent, Virtual Reality, YouTube},
pubstate = {published},
tppubtype = {inproceedings}
}
Live streaming has become increasingly popular, with most streamers presenting their real-life appearance. However, Virtual YouTubers (VTubers), virtual 2D or 3D avatars that are voiced by humans, are emerging as live streamers and attracting a growing viewership. This paper presents the development of a conversational agent, named KawAIi, embodied in a 2D character that, while accurately and promptly responding to user requests, provides an entertaining experience in streaming chat platforms such as YouTube while providing adequate real-time support. The agent relies on the Vicuna 7B GPTQ 4-bit Large Language Model (LLM). In addition, KawAIi uses a BERT-based model for analyzing the sentence generated by the model in terms of conveyed emotion and shows self-emotion awareness through facial expressions. Tested with users, the system has demonstrated a good ability to handle the interaction with the user while maintaining a pleasant user experience. In particular, KawAIi has been evaluated positively in terms of engagement and competence on various topics. The results show the potential of this technology to enrich interactivity in streaming platforms and offer a promising model for future online assistance contexts. © 2024 Copyright for this paper by its authors.
Qin, X.; Weaver, G.
Utilizing Generative AI for VR Exploration Testing: A Case Study Proceedings Article
In: Proc. - ACM/IEEE Int. Conf. Autom. Softw. Eng. Workshops, ASEW, pp. 228–232, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 9798400712494 (ISBN).
Abstract | Links | BibTeX | Tags: Ability testing, Accuracy rate, Case Study, Case-studies, Entity selections, Field of views, Generative adversarial networks, GUI Exploration Testing, GUI testing, Localisation, Long term memory, Mixed data, Object identification, Object recognition, Virtual environments, Virtual Reality
@inproceedings{qin_utilizing_2024,
title = {Utilizing Generative AI for VR Exploration Testing: A Case Study},
author = {X. Qin and G. Weaver},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213332710&doi=10.1145%2F3691621.3694955&partnerID=40&md5=4a05244d58e7be8e1c8adc52b5ffab4a},
doi = {10.1145/3691621.3694955},
isbn = {9798400712494 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - ACM/IEEE Int. Conf. Autom. Softw. Eng. Workshops, ASEW},
pages = {228–232},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {As the virtual reality (VR) industry expands, the need for automated GUI testing for applications is growing rapidly. With its long-term memory and ability to process mixed data, including images and text, Generative AI (GenAI) shows the potential to understand complex user interfaces. In this paper, we conduct a case study to investigate the potential of using GenAI for field of view (FOV) analysis in VR exploration testing. Specifically, we examine how the model can assist in test entity selection and test action suggestions. Our experiments demonstrate that while GPT-4o achieves a 63% accuracy rate in object identification within an arbitrary FOV, it struggles with object organization and localization. We also identify critical contexts that can improve the accuracy of suggested actions across multiple FOVs. Finally, we discuss the limitations found during the experiment and offer insights into future research directions. © 2024 Elsevier B.V., All rights reserved.},
keywords = {Ability testing, Accuracy rate, Case Study, Case-studies, Entity selections, Field of views, Generative adversarial networks, GUI Exploration Testing, GUI testing, Localisation, Long term memory, Mixed data, Object identification, Object recognition, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
As the virtual reality (VR) industry expands, the need for automated GUI testing for applications is growing rapidly. With its long-term memory and ability to process mixed data, including images and text, Generative AI (GenAI) shows the potential to understand complex user interfaces. In this paper, we conduct a case study to investigate the potential of using GenAI for field of view (FOV) analysis in VR exploration testing. Specifically, we examine how the model can assist in test entity selection and test action suggestions. Our experiments demonstrate that while GPT-4o achieves a 63% accuracy rate in object identification within an arbitrary FOV, it struggles with object organization and localization. We also identify critical contexts that can improve the accuracy of suggested actions across multiple FOVs. Finally, we discuss the limitations found during the experiment and offer insights into future research directions. © 2024 Elsevier B.V., All rights reserved.
Rausa, M.; Gaglio, S.; Augello, A.; Caggianese, G.; Franchini, S.; Gallo, L.; Sabatucci, L.
Enriching Metaverse with Memories Through Generative AI: A Case Study Proceedings Article
In: IEEE Int. Conf. Metrol. Ext. Real., Artif. Intell. Neural Eng., MetroXRAINE - Proc., pp. 371–376, Institute of Electrical and Electronics Engineers Inc., St Albans, United Kingdom, 2024, ISBN: 9798350378009 (ISBN).
Abstract | Links | BibTeX | Tags: 3D modeling, 3D models, 3D reconstruction, 3d-modeling, Case-studies, Generative adversarial networks, Generative AI, Input modes, Metamemory, Metaverses, Synthetic Data Generation, Synthetic data generations, Textual description, Virtual environments, Virtual Reality
@inproceedings{rausa_enriching_2024,
title = {Enriching Metaverse with Memories Through Generative AI: A Case Study},
author = {M. Rausa and S. Gaglio and A. Augello and G. Caggianese and S. Franchini and L. Gallo and L. Sabatucci},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216124702&doi=10.1109%2FMetroXRAINE62247.2024.10796338&partnerID=40&md5=086a7e0014d905cc2a4a597e0d28dbca},
doi = {10.1109/MetroXRAINE62247.2024.10796338},
isbn = {9798350378009 (ISBN)},
year = {2024},
date = {2024-01-01},
urldate = {2025-01-07},
booktitle = {IEEE Int. Conf. Metrol. Ext. Real., Artif. Intell. Neural Eng., MetroXRAINE - Proc.},
pages = {371–376},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
address = {St Albans, United Kingdom},
abstract = {The paper introduces MetaMemory, an approach to generate 3D models from either textual descriptions or photographs of objects, offering dual input modes for enhanced representation. MetaMemory's architecture is discussed presenting the tools employed in extracting the object from the image, generating the 3D mesh from texts or images, and visualizing the object reconstruction in an immersive scenario. Afterwards, a case study in which we experienced reconstructing memories of ancient crafts is examined together with the achieved results, by highlighting current limitations and potential applications. © 2025 Elsevier B.V., All rights reserved.},
keywords = {3D modeling, 3D models, 3D reconstruction, 3d-modeling, Case-studies, Generative adversarial networks, Generative AI, Input modes, Metamemory, Metaverses, Synthetic Data Generation, Synthetic data generations, Textual description, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper introduces MetaMemory, an approach to generate 3D models from either textual descriptions or photographs of objects, offering dual input modes for enhanced representation. MetaMemory's architecture is discussed presenting the tools employed in extracting the object from the image, generating the 3D mesh from texts or images, and visualizing the object reconstruction in an immersive scenario. Afterwards, a case study in which we experienced reconstructing memories of ancient crafts is examined together with the achieved results, by highlighting current limitations and potential applications. © 2025 Elsevier B.V., All rights reserved.