AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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Here you can find the complete list of our publications.
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Xing, Y.; Liu, Q.; Wang, J.; Gómez-Zará, D.
sMoRe: Spatial Mapping and Object Rendering Environment Proceedings Article
In: Int Conf Intell User Interfaces Proc IUI, pp. 115–119, Association for Computing Machinery, 2025, ISBN: 979-840071409-2 (ISBN).
Abstract | Links | BibTeX | Tags: Generative adversarial networks, Generative AI, Language Model, Large language model, large language models, Mapping, Mixed reality, Mixed-reality environment, Object rendering, Rendering (computer graphics), Space Manipulation, Spatial mapping, Spatial objects, Users' experiences, Virtual environments, Virtual objects
@inproceedings{xing_smore_2025,
title = {sMoRe: Spatial Mapping and Object Rendering Environment},
author = {Y. Xing and Q. Liu and J. Wang and D. Gómez-Zará},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001670668&doi=10.1145%2f3708557.3716337&partnerID=40&md5=8ef4c5c4ef2b3ee30d00e4b8d19d19b8},
doi = {10.1145/3708557.3716337},
isbn = {979-840071409-2 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Int Conf Intell User Interfaces Proc IUI},
pages = {115–119},
publisher = {Association for Computing Machinery},
abstract = {In mixed reality (MR) environments, understanding space and creating virtual objects is crucial to providing an intuitive user experience. This paper introduces sMoRe (Spatial Mapping and Object Rendering Environment), an MR application that combines Generative AI (GenAI) to assist users in creating, placing, and managing virtual objects within physical spaces. sMoRe allows users to use voice or typed text commands to create and place virtual objects using GenAI while specifying spatial constraints. The system employs Large Language Models (LLMs) to interpret users’ commands, analyze the current scene, and identify optimal locations. Additionally, sMoRe integrates a text-to-3D generative model to dynamically create 3D objects based on users’ descriptions. Our user study demonstrates the effectiveness of sMoRe in enhancing user comprehension, interaction, and organization of the MR environment. © 2025 Copyright held by the owner/author(s).},
keywords = {Generative adversarial networks, Generative AI, Language Model, Large language model, large language models, Mapping, Mixed reality, Mixed-reality environment, Object rendering, Rendering (computer graphics), Space Manipulation, Spatial mapping, Spatial objects, Users' experiences, Virtual environments, Virtual objects},
pubstate = {published},
tppubtype = {inproceedings}
}
In mixed reality (MR) environments, understanding space and creating virtual objects is crucial to providing an intuitive user experience. This paper introduces sMoRe (Spatial Mapping and Object Rendering Environment), an MR application that combines Generative AI (GenAI) to assist users in creating, placing, and managing virtual objects within physical spaces. sMoRe allows users to use voice or typed text commands to create and place virtual objects using GenAI while specifying spatial constraints. The system employs Large Language Models (LLMs) to interpret users’ commands, analyze the current scene, and identify optimal locations. Additionally, sMoRe integrates a text-to-3D generative model to dynamically create 3D objects based on users’ descriptions. Our user study demonstrates the effectiveness of sMoRe in enhancing user comprehension, interaction, and organization of the MR environment. © 2025 Copyright held by the owner/author(s).
2024
Pooryousef, V.; Cordeil, M.; Besançon, L.; Bassed, R.; Dwyer, T.
Collaborative Forensic Autopsy Documentation and Supervised Report Generation using a Hybrid Mixed-Reality Environment and Generative AI Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 11, pp. 7452–7462, 2024, ISSN: 10772626 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Autopsy, Causes of death, Complex procedure, Computer graphics, computer interface, Data visualization, Digital forensics, Documentation, Forensic autopsy, Forensic engineering, Forensic investigation, forensic science, Forensic Sciences, Generative AI, human, Humans, Imaging, Information Management, Laws and legislation, Mixed reality, Mixed-reality environment, Post mortem imaging, procedures, Report generation, Three-Dimensional, three-dimensional imaging, User-Computer Interface, Visualization, Workflow
@article{pooryousef_collaborative_2024,
title = {Collaborative Forensic Autopsy Documentation and Supervised Report Generation using a Hybrid Mixed-Reality Environment and Generative AI},
author = {V. Pooryousef and M. Cordeil and L. Besançon and R. Bassed and T. Dwyer},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204066202&doi=10.1109%2fTVCG.2024.3456212&partnerID=40&md5=d1abaf1aaf3b033df21067ea34b8b98a},
doi = {10.1109/TVCG.2024.3456212},
issn = {10772626 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
volume = {30},
number = {11},
pages = {7452–7462},
abstract = {—Forensic investigation is a complex procedure involving experts working together to establish cause of death and report findings to legal authorities. While new technologies are being developed to provide better post-mortem imaging capabilities—including mixed-reality (MR) tools to support 3D visualisation of such data—these tools do not integrate seamlessly into their existing collaborative workflow and report authoring process, requiring extra steps, e.g. to extract imagery from the MR tool and combine with physical autopsy findings for inclusion in the report. Therefore, in this work we design and evaluate a new forensic autopsy report generation workflow and present a novel documentation system using hybrid mixed-reality approaches to integrate visualisation, voice and hand interaction, as well as collaboration and procedure recording. Our preliminary findings indicate that this approach has the potential to improve data management, aid reviewability, and thus, achieve more robust standards. Further, it potentially streamlines report generation and minimise dependency on external tools and assistance, reducing autopsy time and related costs. This system also offers significant potential for education. A free copy of this paper and all supplemental materials are available at https://osf.io/ygfzx. © 2024 IEEE.},
keywords = {Artificial intelligence, Augmented Reality, Autopsy, Causes of death, Complex procedure, Computer graphics, computer interface, Data visualization, Digital forensics, Documentation, Forensic autopsy, Forensic engineering, Forensic investigation, forensic science, Forensic Sciences, Generative AI, human, Humans, Imaging, Information Management, Laws and legislation, Mixed reality, Mixed-reality environment, Post mortem imaging, procedures, Report generation, Three-Dimensional, three-dimensional imaging, User-Computer Interface, Visualization, Workflow},
pubstate = {published},
tppubtype = {article}
}
—Forensic investigation is a complex procedure involving experts working together to establish cause of death and report findings to legal authorities. While new technologies are being developed to provide better post-mortem imaging capabilities—including mixed-reality (MR) tools to support 3D visualisation of such data—these tools do not integrate seamlessly into their existing collaborative workflow and report authoring process, requiring extra steps, e.g. to extract imagery from the MR tool and combine with physical autopsy findings for inclusion in the report. Therefore, in this work we design and evaluate a new forensic autopsy report generation workflow and present a novel documentation system using hybrid mixed-reality approaches to integrate visualisation, voice and hand interaction, as well as collaboration and procedure recording. Our preliminary findings indicate that this approach has the potential to improve data management, aid reviewability, and thus, achieve more robust standards. Further, it potentially streamlines report generation and minimise dependency on external tools and assistance, reducing autopsy time and related costs. This system also offers significant potential for education. A free copy of this paper and all supplemental materials are available at https://osf.io/ygfzx. © 2024 IEEE.
Xu, S.; Wei, Y.; Zheng, P.; Zhang, J.; Yu, C.
LLM enabled generative collaborative design in a mixed reality environment Journal Article
In: Journal of Manufacturing Systems, vol. 74, pp. 703–715, 2024, ISSN: 02786125 (ISSN).
Abstract | Links | BibTeX | Tags: Collaborative design, Collaborative design process, Communication barriers, Computational Linguistics, design, Design frameworks, generative artificial intelligence, Iterative methods, Language Model, Large language model, Mixed reality, Mixed-reality environment, Multi-modal, Visual languages
@article{xu_llm_2024,
title = {LLM enabled generative collaborative design in a mixed reality environment},
author = {S. Xu and Y. Wei and P. Zheng and J. Zhang and C. Yu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192244873&doi=10.1016%2fj.jmsy.2024.04.030&partnerID=40&md5=3f050c429cf5a4120d10a432311f46cb},
doi = {10.1016/j.jmsy.2024.04.030},
issn = {02786125 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Journal of Manufacturing Systems},
volume = {74},
pages = {703–715},
abstract = {In the collaborative design process, diverse stakeholder backgrounds often introduce inefficiencies in collaboration, such as delays in design delivery and decreased creativity, primarily due to misunderstandings and communication barriers caused by this diversity. To respond, this study proposes an AI-augmented Multimodal Collaborative Design (AI-MCD) framework. This framework utilizes Large Language Models (LLM) to establish an iterative prompting mechanism that provides professional design prompts for Generative AI (GAI) to generate precise visual schemes. On this basis, the GAI cooperates with Mixed Reality (MR) technology to form an interactive and immersive environment for enabling full participation in the design process. By integrating these technologies, the study aims to help stakeholders form a unified cognition and optimize the traditional collaborative design process. Through a case study involving the development of heart education products for children, the effectiveness of the framework is emphasized, and the practical application and effectiveness of the proposed method innovation are demonstrated. © 2024 The Society of Manufacturing Engineers},
keywords = {Collaborative design, Collaborative design process, Communication barriers, Computational Linguistics, design, Design frameworks, generative artificial intelligence, Iterative methods, Language Model, Large language model, Mixed reality, Mixed-reality environment, Multi-modal, Visual languages},
pubstate = {published},
tppubtype = {article}
}
In the collaborative design process, diverse stakeholder backgrounds often introduce inefficiencies in collaboration, such as delays in design delivery and decreased creativity, primarily due to misunderstandings and communication barriers caused by this diversity. To respond, this study proposes an AI-augmented Multimodal Collaborative Design (AI-MCD) framework. This framework utilizes Large Language Models (LLM) to establish an iterative prompting mechanism that provides professional design prompts for Generative AI (GAI) to generate precise visual schemes. On this basis, the GAI cooperates with Mixed Reality (MR) technology to form an interactive and immersive environment for enabling full participation in the design process. By integrating these technologies, the study aims to help stakeholders form a unified cognition and optimize the traditional collaborative design process. Through a case study involving the development of heart education products for children, the effectiveness of the framework is emphasized, and the practical application and effectiveness of the proposed method innovation are demonstrated. © 2024 The Society of Manufacturing Engineers