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{stacchio_minevra_2025,

title = {MineVRA: Exploring the Role of Generative AI-Driven Content Development in XR Environments through a Context-Aware Approach},

author = {L. Stacchio and E. Balloni and E. Frontoni and M. Paolanti and P. Zingaretti and R. Pierdicca},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003746367&doi=10.1109%2fTVCG.2025.3549160&partnerID=40&md5=70b162b574eebbb0cb71db871aa787e1},

doi = {10.1109/TVCG.2025.3549160},

issn = {10772626 (ISSN)},

year  = {2025},

date = {2025-01-01},

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

volume = {31},

number = {5},

pages = {3602–3612},

abstract = {The convergence of Artificial Intelligence (AI), Computer Vision (CV), Computer Graphics (CG), and Extended Reality (XR) is driving innovation in immersive environments. A key challenge in these environments is the creation of personalized 3D assets, traditionally achieved through manual modeling, a time-consuming process that often fails to meet individual user needs. More recently, Generative AI (GenAI) has emerged as a promising solution for automated, context-aware content generation. In this paper, we present MineVRA (Multimodal generative artificial iNtelligence for contExt-aware Virtual Reality Assets), a novel Human-In-The-Loop (HITL) XR framework that integrates GenAI to facilitate coherent and adaptive 3D content generation in immersive scenarios. To evaluate the effectiveness of this approach, we conducted a comparative user study analyzing the performance and user satisfaction of GenAI-generated 3D objects compared to those generated by Sketchfab in different immersive contexts. The results suggest that GenAI can significantly complement traditional 3D asset libraries, with valuable design implications for the development of human-centered XR environments. © 1995-2012 IEEE.},

keywords = {adult, Article, Artificial intelligence, Computer graphics, Computer vision, Content Development, Contents development, Context-Aware, Context-aware approaches, Extended reality, female, Generative adversarial networks, Generative AI, generative artificial intelligence, human, Human-in-the-loop, Immersive, Immersive environment, male, Multi-modal, User need, Virtual environments, 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}

}

@article{franchiniDesignImplementationEmbedded2013,

title = {Design and Implementation of an Embedded Coprocessor with Native Support for 5D, Quadruple-Based Clifford Algebra},

author = { Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},

doi = {10.1109/TC.2012.225},

issn = {0018-9340},

year  = {2013},

date = {2013-01-01},

journal = {IEEE Transactions on Computers},

volume = {62},

number = {12},

pages = {2366--2381},

abstract = {Geometric or Clifford algebra (CA) is a powerful mathematical tool that offers a natural and intuitive way to model geometric facts in a number of research fields, such as robotics, machine vision, and computer graphics. Operating in higher dimensional spaces, its practical use is hindered, however, by a significant computational cost, only partially addressed by dedicated software libraries and hardware/software codesigns. For low-dimensional algebras, several dedicated hardware accelerators and coprocessing architectures have been already proposed in the literature. This paper introduces the architecture of CliffordALU5, an embedded coprocessing core conceived for native execution of up to 5D CA operations. CliffordALU5 exploits a novel, hardware-oriented representation of the algebra elements that allows for faster execution of Clifford operations. In this paper, a prototype implementation of a complete system-on-chip (SOC) based on CliffordALU5 is presented. This prototype integrates an embedded processing soft-core based on the PowerPC 405 and a CliffordALU5 coprocessor on a Xilinx XUPV2P Field Programmable Gate Array (FPGA) board. Test results show a 5texttimes average speedup for 4D Clifford products and a 4texttimes average speedup for 5D Clifford products against the same operations in Gaigen 2, a CA software library generator running on the general-purpose PowerPC processor. This paper also presents an execution analysis of three different applications in three diverse domains, namely, inverse kinematics of a robot, optical motion capture, and raytracing, showing an average speedup between 3texttimes and 4texttimes with respect to the baseline Gaigen 2 implementation. Finally, a multicore approach to higher dimensional CA based on CliffordALU5 is discussed. textcopyright 1968-2012 IEEE.},

keywords = {Application-specific processors, Clifford algebra, Computational geometry, Computer graphics, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, inverse kinematics, Motion capture, Raytracing, robotic arm, Robotics},

pubstate = {published},

tppubtype = {article}

}

@article{franchini_design_2013,

title = {Design and implementation of an embedded coprocessor with native support for 5D, quadruple-based clifford algebra},

author = {Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},

doi = {10.1109/TC.2012.225},

issn = {0018-9340},

year  = {2013},

date = {2013-01-01},

journal = {IEEE Transactions on Computers},

volume = {62},

number = {12},

pages = {2366–2381},

abstract = {Geometric or Clifford algebra (CA) is a powerful mathematical tool that offers a natural and intuitive way to model geometric facts in a number of research fields, such as robotics, machine vision, and computer graphics. Operating in higher dimensional spaces, its practical use is hindered, however, by a significant computational cost, only partially addressed by dedicated software libraries and hardware/software codesigns. For low-dimensional algebras, several dedicated hardware accelerators and coprocessing architectures have been already proposed in the literature. This paper introduces the architecture of CliffordALU5, an embedded coprocessing core conceived for native execution of up to 5D CA operations. CliffordALU5 exploits a novel, hardware-oriented representation of the algebra elements that allows for faster execution of Clifford operations. In this paper, a prototype implementation of a complete system-on-chip (SOC) based on CliffordALU5 is presented. This prototype integrates an embedded processing soft-core based on the PowerPC 405 and a CliffordALU5 coprocessor on a Xilinx XUPV2P Field Programmable Gate Array (FPGA) board. Test results show a 5× average speedup for 4D Clifford products and a 4× average speedup for 5D Clifford products against the same operations in Gaigen 2, a CA software library generator running on the general-purpose PowerPC processor. This paper also presents an execution analysis of three different applications in three diverse domains, namely, inverse kinematics of a robot, optical motion capture, and raytracing, showing an average speedup between 3× and 4× with respect to the baseline Gaigen 2 implementation. Finally, a multicore approach to higher dimensional CA based on CliffordALU5 is discussed. © 1968-2012 IEEE.},

keywords = {Application-specific processors, Clifford algebra, Computational geometry, Computer graphics, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, inverse kinematics, Motion capture, Raytracing, robotic arm, Robotics},

pubstate = {published},

tppubtype = {article}

}