Publications

@article{li_xs_2025,

title = {X’s Day: Personality-Driven Virtual Human Behavior Generation},

author = {H. Li and Z. Wang and W. Liang and Y. Wang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003864932&doi=10.1109%2fTVCG.2025.3549574&partnerID=40&md5=a865bbd2b0fa964a4f0f4190955dc787},

doi = {10.1109/TVCG.2025.3549574},

issn = {10772626 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Transactions on Visualization and Computer Graphics},

volume = {31},

number = {5},

pages = {3514–3524},

abstract = {Developing convincing and realistic virtual human behavior is essential for enhancing user experiences in virtual reality (VR) and augmented reality (AR) settings. This paper introduces a novel task focused on generating long-term behaviors for virtual agents, guided by specific personality traits and contextual elements within 3D environments. We present a comprehensive framework capable of autonomously producing daily activities autoregressively. By modeling the intricate connections between personality characteristics and observable activities, we establish a hierarchical structure of Needs, Task, and Activity levels. Integrating a Behavior Planner and a World State module allows for the dynamic sampling of behaviors using large language models (LLMs), ensuring that generated activities remain relevant and responsive to environmental changes. Extensive experiments validate the effectiveness and adaptability of our approach across diverse scenarios. This research makes a significant contribution to the field by establishing a new paradigm for personalized and context-aware interactions with virtual humans, ultimately enhancing user engagement in immersive applications. Our project website is at: https://behavior.agent-x.cn/. © 2025 IEEE. All rights reserved,},

keywords = {adult, Augmented Reality, Behavior Generation, Chatbots, Computer graphics, computer interface, Contextual Scene, female, human, Human behaviors, Humans, Long-term behavior, male, Novel task, Personality, Personality traits, Personality-driven Behavior, physiology, Social behavior, User-Computer Interface, Users' experiences, Virtual agent, Virtual environments, Virtual humans, Virtual Reality, Young Adult},

pubstate = {published},

tppubtype = {article}

}

@article{song_enhancing_2025,

title = {Enhancing Patient Acceptance of Robotic Ultrasound through Conversational Virtual Agent and Immersive Visualizations},

author = {T. Song and F. Pabst and U. Eck and N. Navab},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003687673&doi=10.1109%2fTVCG.2025.3549181&partnerID=40&md5=1d46569933582ecf5e967f0794aafc07},

doi = {10.1109/TVCG.2025.3549181},

issn = {10772626 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Transactions on Visualization and Computer Graphics},

volume = {31},

number = {5},

pages = {2901–2911},

abstract = {Robotic ultrasound systems have the potential to improve medical diagnostics, but patient acceptance remains a key challenge. To address this, we propose a novel system that combines an AI-based virtual agent, powered by a large language model (LLM), with three mixed reality visualizations aimed at enhancing patient comfort and trust. The LLM enables the virtual assistant to engage in natural, conversational dialogue with patients, answering questions in any format and offering real-time reassurance, creating a more intelligent and reliable interaction. The virtual assistant is animated as controlling the ultrasound probe, giving the impression that the robot is guided by the assistant. The first visualization employs augmented reality (AR), allowing patients to see the real world and the robot with the virtual avatar superimposed. The second visualization is an augmented virtuality (AV) environment, where the real-world body part being scanned is visible, while a 3D Gaussian Splatting reconstruction of the room, excluding the robot, forms the virtual environment. The third is a fully immersive virtual reality (VR) experience, featuring the same 3D reconstruction but entirely virtual, where the patient sees a virtual representation of their body being scanned in a robot-free environment. In this case, the virtual ultrasound probe, mirrors the movement of the probe controlled by the robot, creating a synchronized experience as it touches and moves over the patient's virtual body. We conducted a comprehensive agent-guided robotic ultrasound study with all participants, comparing these visualizations against a standard robotic ultrasound procedure. Results showed significant improvements in patient trust, acceptance, and comfort. Based on these findings, we offer insights into designing future mixed reality visualizations and virtual agents to further enhance patient comfort and acceptance in autonomous medical procedures. © 1995-2012 IEEE.},

keywords = {3D reconstruction, adult, Augmented Reality, Computer graphics, computer interface, echography, female, human, Humans, Imaging, Intelligent robots, Intelligent virtual agents, Language Model, male, Medical robotics, Middle Aged, Mixed reality, Patient Acceptance of Health Care, patient attitude, Patient comfort, procedures, Real-world, Reality visualization, Robotic Ultrasound, Robotics, Three-Dimensional, three-dimensional imaging, Trust and Acceptance, Ultrasonic applications, Ultrasonic equipment, Ultrasonography, Ultrasound probes, User-Computer Interface, Virtual agent, Virtual assistants, Virtual environments, Virtual Reality, Visual languages, Visualization, Young Adult},

pubstate = {published},

tppubtype = {article}

}

@article{chen_llmer_2025,

title = {LLMER: Crafting Interactive Extended Reality Worlds with JSON Data Generated by Large Language Models},

author = {J. Chen and X. Wu and T. Lan and B. Li},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003825793&doi=10.1109%2fTVCG.2025.3549549&partnerID=40&md5=da4681d0714548e3a7e0c8c3295d2348},

doi = {10.1109/TVCG.2025.3549549},

issn = {10772626 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Transactions on Visualization and Computer Graphics},

volume = {31},

number = {5},

pages = {2715–2724},

abstract = {The integration of Large Language Models (LLMs) like GPT-4 with Extended Reality (XR) technologies offers the potential to build truly immersive XR environments that interact with human users through natural language, e.g., generating and animating 3D scenes from audio inputs. However, the complexity of XR environments makes it difficult to accurately extract relevant contextual data and scene/object parameters from an overwhelming volume of XR artifacts. It leads to not only increased costs with pay-per-use models, but also elevated levels of generation errors. Moreover, existing approaches focusing on coding script generation are often prone to generation errors, resulting in flawed or invalid scripts, application crashes, and ultimately a degraded user experience. To overcome these challenges, we introduce LLMER, a novel framework that creates interactive XR worlds using JSON data generated by LLMs. Unlike prior approaches focusing on coding script generation, LLMER translates natural language inputs into JSON data, significantly reducing the likelihood of application crashes and processing latency. It employs a multi-stage strategy to supply only the essential contextual information adapted to the user's request and features multiple modules designed for various XR tasks. Our preliminary user study reveals the effectiveness of the proposed system, with over 80% reduction in consumed tokens and around 60% reduction in task completion time compared to state-of-the-art approaches. The analysis of users' feedback also illuminates a series of directions for further optimization. © 1995-2012 IEEE.},

keywords = {% reductions, 3D modeling, algorithm, Algorithms, Augmented Reality, Coding errors, Computer graphics, Computer interaction, computer interface, Computer simulation languages, Extended reality, generative artificial intelligence, human, Human users, human-computer interaction, Humans, Imaging, Immersive, Language, Language Model, Large language model, large language models, Metadata, Natural Language Processing, Natural language processing systems, Natural languages, procedures, Script generation, Spatio-temporal data, Three dimensional computer graphics, Three-Dimensional, three-dimensional imaging, User-Computer Interface, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{hassoulas_pilot_2025,

title = {A pilot study investigating the efficacy of technology enhanced case based learning (CBL) in small group teaching},

author = {A. Hassoulas and O. Crawford and S. Hemrom and A. Almeida and M. J. Coffey and M. Hodgson and B. Leveridge and D. Karwa and A. Lethbridge and H. Williams and A. Voisey and K. Reed and S. Patel and K. Hart and H. Shaw},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004223025&doi=10.1038%2fs41598-025-99764-5&partnerID=40&md5=8588cac4c3ffe437e667ba4373e010ec},

doi = {10.1038/s41598-025-99764-5},

issn = {20452322 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Scientific Reports},

volume = {15},

number = {1},

abstract = {The recent paradigm shift in teaching provision within higher education, following the COVID-19 pandemic, has led to blended models of learning prevailing in the pedagogic literature and in education practice. This shift has also resulted in an abundance of tools and technologies coming to market. Whilst the value of integrating technology into teaching and assessment has been well-established in the literature, the magnitude of choice available to educators and to students can be overwhelming. The current pilot investigated the feasibility of integrating key technologies in delivering technology-enhanced learning (TEL) case-based learning (CBL) within a sample of year two medical students. The cohort was selected at random, as was the control group receiving conventional CBL. Both groups were matched on prior academic performance. The TEL-CBL group received (1) in-person tutorials delivered within an immersive learning suite, (2) access to 3D anatomy software to explore during their self-directed learning time, (3) virtual reality (VR) guided anatomy exploration during tutorials, (4) access to a generative AI-based simulated virtual patient repository to practice key skills such as communication and history taking, and (5) an immersive medical emergency simulation. Metrics assessed included formative academic performance, student learning experience, and confidence in relation to communication and clinical skills. The results revealed that the TEL-CBL group outperformed their peers in successive formative assessments (p < 0.05), engaged thoroughly with the technologies at their disposal, and reported that these technologies enhanced their learning experience. Furthermore, students reported that access to the GenAI-simulated virtual patient platform and the immersive medical emergency simulation improved their clinical confidence and gave them a useful insight into what they can expect during the clinical phase of their medical education. The results are discussed in relation to the advantages that key emerging technologies may play in enhancing student performance, experience and confidence. © The Author(s) 2025.},

keywords = {coronavirus disease 2019, Covid-19, epidemiology, female, human, Humans, Learning, male, Medical, Medical student, Pilot Projects, pilot study, problem based learning, Problem-Based Learning, procedures, SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2, Students, Teaching, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@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}

}