Publications

@inproceedings{behravan_generative_2025,

title = {Generative AI for Context-Aware 3D Object Creation Using Vision-Language Models in Augmented Reality},

author = {M. Behravan and K. Matković and D. Grǎcanin},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000292700&doi=10.1109%2FAIxVR63409.2025.00018&partnerID=40&md5=0a11897a4f37fd8ebaa257498cb92eb7},

doi = {10.1109/AIxVR63409.2025.00018},

isbn = {9798331521578 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},

pages = {73–81},

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

abstract = {We present a novel Artificial Intelligence (AI) system that functions as a designer assistant in augmented reality (AR) environments. Leveraging Vision Language Models (VLMs) like LLaVA and advanced text-to-3D generative models, users can capture images of their surroundings with an Augmented Reality (AR) headset. The system analyzes these images to recommend contextually relevant objects that enhance both functionality and visual appeal. The recommended objects are generated as 3D models and seamlessly integrated into the AR environment for interactive use. Our system utilizes open-source AI models running on local systems to enhance data security and reduce operational costs. Key features include context-aware object suggestions, optimal placement guidance, aesthetic matching, and an intuitive user interface for real-time interaction. Evaluations using the COCO 2017 dataset and real-world AR testing demonstrated high accuracy in object detection and contextual fit rating of 4.1 out of 5. By addressing the challenge of providing context-aware object recommendations in AR, our system expands the capabilities of AI applications in this domain. It enables users to create personalized digital spaces efficiently, leveraging AI for contextually relevant suggestions. © 2025 Elsevier B.V., All rights reserved.},

keywords = {3D object, 3D Object Generation, Artificial intelligence systems, Augmented Reality, Capture images, Context-Aware, Generative adversarial networks, Generative AI, generative artificial intelligence, Generative model, Language Model, Object creation, Vision language model, vision language models, Visual languages},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gatti_around_2025,

title = {Around the Virtual Campfire: Early UX Insights into AI-Generated Stories in VR},

author = {E. Gatti and D. Giunchi and N. Numan and A. Steed},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000263662&doi=10.1109%2FAIxVR63409.2025.00027&partnerID=40&md5=ab95e803af14233db6ed307222632542},

doi = {10.1109/AIxVR63409.2025.00027},

isbn = {9798331521578 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},

pages = {136–141},

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

abstract = {Virtual Reality (VR) presents an immersive platform for storytelling, allowing narratives to unfold in highly engaging, interactive environments. Leveraging AI capabilities and image synthesis offers new possibilities for creating scalable, generative VR content. In this work, we use an LLM-driven VR storytelling platform to explore how AI-generated visuals and narrative elements impact the user experience in VR storytelling. Previously, we presented AIsop, a system to integrate LLM-generated text and images and TTS audio into a storytelling experience, where the narrative unfolds based on user input. In this paper, we present two user studies focusing on how AI-generated visuals influence narrative perception and the overall VR experience. Our findings highlight the positive impact of AI-generated pictorial content on the storytelling experience, highlighting areas for enhancement and further research in interactive narrative design. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Generative AI, Images synthesis, Immersive, Interactive Environments, Language Model, Large language model, Storytelling, User input, User study, Users' experiences, Virtual environments, VR},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{angelopoulos_industrial_2025,

title = {Industrial Maintenance Optimization Based on the Integration of Large Language Models (LLM) and Augmented Reality (AR)},

author = {J. Angelopoulos and C. Manettas and K. Alexopoulos},

editor = {Alexopoulos K. and Makris S. and Stavropoulos P.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001421726&doi=10.1007%2f978-3-031-86489-6_20&partnerID=40&md5=63be31b9f4dda4aafd6a641630506c09},

doi = {10.1007/978-3-031-86489-6_20},

isbn = {21954356 (ISSN); 978-303186488-9 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Mech. Eng.},

pages = {197–205},

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

abstract = {Traditional maintenance procedures often rely on manual data processing and human expertise, leading to inefficiencies and potential errors. In the context of Industry 4.0 several digital technologies, such as Artificial Intelligence (AI), Big Data Analytics (BDA), and eXtended Reality (XR) have been developed and are constantly being integrated in a plethora of manufacturing activities (including industrial maintenance), in an attempt to minimize human error, facilitate shop floor technicians, reduce costs as well as reduce equipment downtimes. The latest developments in the field of AI point towards Large Language Models (LLM) which can communicate with human operators in an intuitive manner. On the other hand, Augmented Reality, as part of XR technologies, offers useful functionalities for improving user perception and interaction with modern, complex industrial equipment. Therefore, the context of this research work lies in the development and training of an LLM in order to provide suggestions and actionable items for the mitigation of unforeseen events (e.g. equipment breakdowns), in order to facilitate shop-floor technicians during their everyday tasks. Paired with AR visualizations over the physical environment, the technicians will get instructions for performing tasks and checks on the industrial equipment in a manner similar to human-to-human communication. The functionality of the proposed framework extends to the integration of modules for exchanging information with the engineering department towards the scheduling of Maintenance and Repair Operations (MRO) as well as the creation of a repository of historical data in order to constantly retrain and optimize the LLM. © The Author(s) 2025.},

keywords = {Augmented Reality, Competition, Cost reduction, Critical path analysis, Crushed stone plants, Generative AI, generative artificial intelligence, Human expertise, Industrial equipment, Industrial maintenance, Language Model, Large language model, Maintenance, Maintenance optimization, Maintenance procedures, Manufacturing data processing, Potential errors, Problem oriented languages, Scheduled maintenance, Shopfloors, Solar power plants},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{paduraru_generative_2025,

title = {Generative AI for Human 3D Body Emotions: A Dataset and Baseline Methods},

author = {C. Paduraru and P. -L. Bouruc and A. Stefanescu},

editor = {Rocha A.P. and Steels L. and Herik H.J.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001951577&doi=10.5220%2f0013168700003890&partnerID=40&md5=7fa058a0c9ec8275083b55e8990a8d22},

doi = {10.5220/0013168700003890},

isbn = {21843589 (ISSN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Int. Conf. Agent. Artif. Intell.},

volume = {3},

pages = {646–653},

publisher = {Science and Technology Publications, Lda},

abstract = {Accurate and expressive representation of human emotions in 3D models remains a major challenge in various industries, including gaming, film, healthcare, virtual reality and robotics. This work aims to address this challenge by utilizing a new dataset and a set of baseline methods within an open-source framework developed to improve realism and emotional expressiveness in human 3D representations. At the center of this work is the use of a novel and diverse dataset consisting of short video clips showing people mimicking specific emotions: anger, happiness, surprise, disgust, sadness, and fear. The dataset was further processed using state-of-theart parametric body models that accurately reproduce these emotions. The resulting 3D meshes were then integrated into a generative pose generation model capable of producing similar emotions. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.},

keywords = {Animations, Body Emotions, Generative AI, Parametric Models},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{li_enhancing_2025,

title = {Enhancing Linear Algebra Education with AI-Generated Content in the CityU Metaverse: A Comparative Study},

author = {Y. Li and E. C. H. Pang and C. S. Y. Ng and M. Azim and H. Leung},

editor = {Hao T. and Wu J.G. and Luo X. and Sun Y. and Mu Y. and Ge S. and Xie W.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003632691&doi=10.1007%2f978-981-96-4407-0_1&partnerID=40&md5=c067ba5d4c15e9c0353bf315680531fc},

doi = {10.1007/978-981-96-4407-0_1},

isbn = {03029743 (ISSN); 978-981964406-3 (ISBN)},

year  = {2025},

date = {2025-01-01},

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

volume = {15589 LNCS},

pages = {3–16},

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

abstract = {In today’s digital age, the metaverse is emerging as the forthcoming evolution of the internet. It provides an immersive space that marks a new frontier in the way digital interactions are facilitated and experienced. In this paper, we present the CityU Metaverse, which aims to construct a digital twin of our university campus. It is designed as an educational virtual world where learning applications can be embedded in this virtual campus, supporting not only remote and collaborative learning but also professional technical training to enhance educational experiences through immersive and interactive learning. To evaluate the effectiveness of this educational metaverse, we conducted an experiment focused on 3D linear transformation in linear algebra, with teaching content generated by generative AI, comparing our metaverse system with traditional teaching methods. Knowledge tests and surveys assessing learning interest revealed that students engaged with the CityU Metaverse, facilitated by AI-generated content, outperformed those in traditional settings and reported greater enjoyment during the learning process. The work provides valuable perspectives on the behaviors and interactions within the metaverse by analyzing user preferences and learning outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.},

keywords = {Comparatives studies, Digital age, Digital interactions, digital twin, Educational metaverse, Engineering education, Generative AI, Immersive, Matrix algebra, Metaverse, Metaverses, Personnel training, Students, Teaching, University campus, Virtual environments, virtual learning environment, Virtual learning environments, Virtual Reality, Virtualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{li_exploring_2025,

title = {Exploring Large Language Model-Driven Agents for Environment-Aware Spatial Interactions and Conversations in Virtual Reality Role-Play Scenarios},

author = {Z. Li and H. Zhang and C. Peng and R. Peiris},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002706893&doi=10.1109%2FVR59515.2025.00025&partnerID=40&md5=1987c128f6ec4bd24011388ef9ece179},

doi = {10.1109/VR59515.2025.00025},

isbn = {9798331536459 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces, VR},

pages = {1–11},

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

abstract = {Recent research has begun adopting Large Language Model (LLM) agents to enhance Virtual Reality (VR) interactions, creating immersive chatbot experiences. However, while current studies focus on generating dialogue from user speech inputs, their abilities to generate richer experiences based on the perception of LLM agents' VR environments and interaction cues remain unexplored. Hence, in this work, we propose an approach that enables LLM agents to perceive virtual environments and generate environment-aware interactions and conversations for an embodied human-AI interaction experience in VR environments. Here, we define a schema for describing VR environments and their interactions through text prompts. We evaluate the performance of our method through five role-play scenarios created using our approach in a study with 14 participants. The findings discuss the opportunities and challenges of our proposed approach for developing environment-aware LLM agents that facilitate spatial interactions and conversations within VR role-play scenarios. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Chatbots, Computer simulation languages, Context- awareness, context-awareness, Digital elevation model, Generative AI, Human-AI Interaction, Language Model, Large language model, large language models, Model agents, Role-play simulation, role-play simulations, Role-plays, Spatial interaction, Virtual environments, Virtual Reality, Virtual-reality environment},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{scofano_social_2025,

title = {Social EgoMesh Estimation},

author = {L. Scofano and A. Sampieri and E. Matteis and I. Spinelli and F. Galasso},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003632729&doi=10.1109%2FWACV61041.2025.00580&partnerID=40&md5=95666e3f74ddb6b96666a80eace686c5},

doi = {10.1109/WACV61041.2025.00580},

isbn = {9798331510831 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Proc. - IEEE Winter Conf. Appl. Comput. Vis., WACV},

pages = {5948–5958},

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

abstract = {Accurately estimating the 3D pose of the camera wearer in egocentric video sequences is crucial to modeling human behavior in virtual and augmented reality applications. The task presents unique challenges due to the limited visibility of the user's body caused by the front-facing camera mounted on their head. Recent research has explored the utilization of the scene and ego-motion, but it has overlooked humans' interactive nature. We propose a novel framework for Social Egocentric Estimation of body MEshes (SEE-ME). Our approach is the first to estimate the wearer's mesh using only a latent probabilistic diffusion model, which we condition on the scene and, for the first time, on the social wearer-interactee interactions. Our in-depth study sheds light on when social interaction matters most for ego-mesh estimation; it quantifies the impact of interpersonal distance and gaze direction. Overall, SEEME surpasses the current best technique, reducing the pose estimation error (MPJPE) by 53%. The code is available at SEEME. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Augmented reality applications, Ego-motion, Egocentric view, Generative AI, Human behaviors, Human mesh recovery, Limited visibility, Recent researches, Three dimensional computer graphics, Video sequences, Virtual and augmented reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sousa_transformative_2025,

title = {Transformative Technologies for Rehabilitation: Leveraging Immersive and AI-Driven Solutions to Reduce Recidivism and Promote Decent Work},

author = {R. T. Sousa and E. A. M. Oliveira and L. M. F. Cintra and A. R. G. Galvão Filho},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005140551&doi=10.1109%2FVRW66409.2025.00042&partnerID=40&md5=a8dbe15493fd8361602d049f2b09efe3},

doi = {10.1109/VRW66409.2025.00042},

isbn = {9798331514846 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},

pages = {168–171},

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

abstract = {The reintegration of incarcerated individuals into society presents significant challenges, particularly in addressing barriers related to vocational training, social skill development, and emotional rehabilitation. Immersive technologies, such as Virtual Reality and Augmented Reality, combined with generative Artificial Intelligence (AI) and Large Language Models, offer innovative opportunities to enhance these areas. These technologies create practical, controlled environments for skill acquisition and behavioral training, while generative AI enables dynamic, personalized, and adaptive experiences. This paper explores the broader potential of these integrated technologies in supporting rehabilitation, reducing recidivism, and fostering sustainable employment opportunities and these initiatives align with the overarching equity objective of ensuring Decent Work for All, reinforcing the commitment to inclusive and equitable progress across diverse communities, through the transformative potential of immersive and AI-driven systems in correctional systems. © 2025 Elsevier B.V., All rights reserved.},

keywords = {AI- Driven Rehabilitation, Artificial intelligence- driven rehabilitation, Emotional intelligence, Engineering education, Generative AI, generative artificial intelligence, Immersive, Immersive technologies, Immersive Technology, Language Model, Large language model, large language models, Skills development, Social Reintegration, Social skills, Sociology, Vocational training},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lopes_generative_2025,

title = {Generative AI for Personalized Multisensory Immersive Experiences: Challenges and Opportunities for Stress Reduction},

author = {M. K. S. Lopes and T. H. Falk},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005149501&doi=10.1109%2FVRW66409.2025.00036&partnerID=40&md5=9507cf2dcec341c434e08e8b6f92bfda},

doi = {10.1109/VRW66409.2025.00036},

isbn = {9798331514846 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},

pages = {143–146},

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

abstract = {Stress management and relaxation are critical areas of interest in mental health and well-being. Forest bathing is a practice that has been shown to have a positive effect on reducing stress by stimulating all the senses in an immersive nature experience. Since access to nature is not universally available to everyone, virtual reality has emerged as a promising tool to simulate this type of experience. Furthermore, generative artificial intelligence (GenAI) tools offer new opportunities to create highly personalized and immersive experiences that can enhance relaxation and reduce stress. This study explores the potential of personalized multisensory VR environments, designed using GenAI tools, to optimize relaxation and stress relief via two experiments that are currently underway. The first evaluates the effectiveness of non-personalized versus personalized VR scenes generated using AI tools to promote increased relaxation. The second explores the potential benefits of providing the user with additional personalization tools, from adding new virtual elements to the AI-generated scene, to adding AI-generated sounds and scent/haptics customization. Ultimately, this research aims to identify which customizable elements may lead to improved therapeutic benefits for multisensory VR experiences. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Artificial intelligence tools, Environment personalization, Forest bathing, Generative AI, Immersive, Multi-Sensory, Multi-sensory virtual reality, Multisensory, Personalizations, Relaxation, Virtual Reality, Virtualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{behravan_voices_2025,

title = {From Voices to Worlds: Developing an AI-Powered Framework for 3D Object Generation in Augmented Reality},

author = {M. Behravan and D. Grǎcanin},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005153589&doi=10.1109%2FVRW66409.2025.00038&partnerID=40&md5=34311e63349697801caf849bc231e879},

doi = {10.1109/VRW66409.2025.00038},

isbn = {9798331514846 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},

pages = {150–155},

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

abstract = {This paper presents Matrix, an advanced AI-powered framework designed for real-time 3D object generation in Augmented Reality (AR) environments. By integrating a cutting-edge text-to-3D generative AI model, multilingual speech-to-text translation, and large language models (LLMs), the system enables seamless user interactions through spoken commands. The framework processes speech inputs, generates 3D objects, and provides object recommendations based on contextual understanding, enhancing AR experiences. A key feature of this framework is its ability to optimize 3D models by reducing mesh complexity, resulting in significantly smaller file sizes and faster processing on resource-constrained AR devices. Our approach addresses the challenges of high GPU usage, large model output sizes, and real-time system responsiveness, ensuring a smoother user experience. Moreover, the system is equipped with a pre-generated object repository, further reducing GPU load and improving efficiency. We demonstrate the practical applications of this framework in various fields such as education, design, and accessibility, and discuss future enhancements including image-to-3D conversion, environmental object detection, and multimodal support. The open-source nature of the framework promotes ongoing innovation and its utility across diverse industries. © 2025 Elsevier B.V., All rights reserved.},

keywords = {3D modeling, 3D object, 3D Object Generation, 3D reconstruction, Augmented Reality, Cutting edges, Generative AI, Interactive computer systems, Language Model, Large language model, large language models, matrix, Multilingual speech interaction, Real- time, Speech enhancement, Speech interaction, Volume Rendering},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kurai_implementation_2025,

title = {An implementation of MagicCraft: Generating Interactive 3D Objects and Their Behaviors from Text for Commercial Metaverse Platforms},

author = {R. Kurai and T. Hiraki and Y. Hiroi and Y. Hirao and M. Perusquía-Hernández and H. Uchiyama and K. Kiyokawa},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005153642&doi=10.1109%2FVRW66409.2025.00288&partnerID=40&md5=fd6d3b8d0dcbc5b9ccd4c31069bb4f4a},

doi = {10.1109/VRW66409.2025.00288},

isbn = {9798331514846 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},

pages = {1284–1285},

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

abstract = {Metaverse platforms are rapidly evolving to provide immersive spaces. However, the generation of dynamic and interactive 3D objects remains a challenge due to the need for advanced 3D modeling and programming skills. We present MagicCraft, a system that generates functional 3D objects from natural language prompts. MagicCraft uses generative AI models to manage the entire content creation pipeline: converting user text descriptions into images, transforming images into 3D models, predicting object behavior, and assigning necessary attributes and scripts. It also provides an interactive interface for users to refine generated objects by adjusting features like orientation, scale, seating positions, and grip points. © 2025 Elsevier B.V., All rights reserved.},

keywords = {3D modeling, 3D models, 3D object, 3D Object Generation, 3d-modeling, AI-Assisted Design, Generative AI, Immersive, Metaverse, Metaverses, Model skill, Object oriented programming, Programming skills},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{behravan_transcending_2025,

title = {Transcending Dimensions Using Generative AI: Real-Time 3D Model Generation in Augmented Reality},

author = {M. Behravan and M. Haghani and D. Gračanin},

editor = {Chen J.Y.C. and Fragomeni G.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007690904&doi=10.1007%2f978-3-031-93700-2_2&partnerID=40&md5=1c4d643aad88d08cbbc9dd2c02413f10},

doi = {10.1007/978-3-031-93700-2_2},

isbn = {03029743 (ISSN); 978-303193699-9 (ISBN)},

year  = {2025},

date = {2025-01-01},

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

volume = {15788 LNCS},

pages = {13–32},

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

abstract = {Traditional 3D modeling requires technical expertise, specialized software, and time-intensive processes, making it inaccessible for many users. Our research aims to lower these barriers by combining generative AI and augmented reality (AR) into a cohesive system that allows users to easily generate, manipulate, and interact with 3D models in real time, directly within AR environments. Utilizing cutting-edge AI models like Shap-E, we address the complex challenges of transforming 2D images into 3D representations in AR environments. Key challenges such as object isolation, handling intricate backgrounds, and achieving seamless user interaction are tackled through advanced object detection methods, such as Mask R-CNN. Evaluation results from 35 participants reveal an overall System Usability Scale (SUS) score of 69.64, with participants who engaged with AR/VR technologies more frequently rating the system significantly higher, at 80.71. This research is particularly relevant for applications in gaming, education, and AR-based e-commerce, offering intuitive, model creation for users without specialized skills. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {3D Model Generation, 3D modeling, 3D models, 3d-modeling, Augmented Reality, Generative AI, Image-to-3D conversion, Model generation, Object Detection, Object recognition, Objects detection, Real- time, Specialized software, Technical expertise, Three dimensional computer graphics, Usability engineering},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{otsuka_development_2025,

title = {Development of A Relaxation Support System Utilizing Stereophonic AR},

author = {T. Otsuka and D. Li and P. Siriaraya and S. Nakajima},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105006602014&doi=10.1109%2FICNC64010.2025.10993739&partnerID=40&md5=72d1dea49b65a396c9a6788ce1ed3274},

doi = {10.1109/ICNC64010.2025.10993739},

isbn = {9798331520960 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Int. Conf. Comput., Netw. Commun., ICNC},

pages = {463–467},

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

abstract = {Given the high prevalence of stress and anxiety in today's society, there is an urgent need to explore effective methods to help people manage stress. This research aims to develop a relaxation support system using stereophonic augmented reality (AR), designed to help alleviate stress by recreating relaxing environments with immersive stereo soundscapes, including stories created from generative AI and environmental sounds while users are going for a walk. This paper presents a preliminary evaluation of the effectiveness of the proposed relaxation support method. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Augmented Reality, Environmental sounds, Generative AI, Immersive, Mental Well-being, Soundscapes, Spatial Audio, Stereo image processing, Support method, Support systems, Well being},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{chang_enhancing_2025,

title = {Enhancing Virtual Restorative Environment with Generative AI: Personalized Immersive Stress-Relief Experiences},

author = {K. -Y. Chang and C. -F. Lee},

editor = {Duffy V.G.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007759157&doi=10.1007%2f978-3-031-93502-2_9&partnerID=40&md5=ee620a5da9b65e90ccb1eaa75ec8b724},

doi = {10.1007/978-3-031-93502-2_9},

isbn = {03029743 (ISSN); 978-303193501-5 (ISBN)},

year  = {2025},

date = {2025-01-01},

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

volume = {15791 LNCS},

pages = {132–144},

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

abstract = {In today’s fast-paced world, stress and mental health challenges are becoming more common. Restorative environments help people relax and recover emotionally, and Virtual Reality Exposure Therapy (VRET) offers a way to experience these benefits beyond physical limitations. However, most VRET applications rely on pre-designed content, limiting their adaptability to individual needs. This study explores how Generative AI can enhance VRET by creating personalized, immersive environments that better match users’ preferences and improve relaxation. To evaluate the impact of AI-generated restorative environments, we combined EEG measurements with user interviews. Thirty university students participated in the study, experiencing two different modes: static mode and walking mode. The EEG results showed an increase in Theta (θ) and High Beta (β) brain waves, suggesting a state of deep immersion accompanied by heightened cognitive engagement and mental effort. While participants found the experience enjoyable and engaging, the AI-generated environments tended to create excitement and focus rather than conventional relaxation. These findings suggest that for AI-generated environments in VRET to be more effective for stress relief, future designs should reduce cognitive load while maintaining immersion. This study provides insights into how AI can enhance relaxation experiences and introduces a new perspective on personalized digital stress-relief solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {Artificial intelligence generated content, Artificial Intelligence Generated Content (AIGC), Electroencephalography, Electroencephalography (EEG), Generative AI, Immersive, Immersive environment, Mental health, Physical limitations, Restorative environment, Stress relief, Virtual reality exposure therapies, Virtual reality exposure therapy, Virtual Reality Exposure Therapy (VRET), Virtualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{yokoyama_vigen_2025,

title = {ViGen: Defamiliarizing Everyday Perception for Discovering Unexpected Insights},

author = {N. Yokoyama and R. Kimura and T. Nakajima},

editor = {Degen H. and Ntoa S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007760030&doi=10.1007%2f978-3-031-93418-6_26&partnerID=40&md5=dee6f54688284313a45579aab5f934d6},

doi = {10.1007/978-3-031-93418-6_26},

isbn = {03029743 (ISSN); 978-303193417-9 (ISBN)},

year  = {2025},

date = {2025-01-01},

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

volume = {15821 LNAI},

pages = {397–417},

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

abstract = {This paper proposes ViGen, an Augmented Reality (AR) and Artificial Intelligence (AI)-enhanced vision system designed to facilitate defamiliarization in daily life. Humans rely on sight to gather information, think, and act, yet the act of seeing often becomes passive in daily life. Inspired by Victor Shklovsky’s concept of defamiliarization and the artistic technique of photomontage, ViGen seeks to disrupt habitual perceptions. It achieves this by overlaying semi-transparent, AI-generated images, created based on the user’s view, through an AR display. The system is evaluated by several structured interviews, in which participants experience ViGen in three different scenarios. Results indicate that AI-generated visuals effectively supported defamiliarization by transforming ordinary scenes into unfamiliar ones. However, the user’s familiarity with a place plays a significant role. Also, while the feature that adjusts the transparency of overlaid images enhances safety, its limitations in dynamic environments suggest the need for further research across diverse cultural and geographic contexts. This study demonstrates the potential of AI-augmented vision systems to stimulate new ways of seeing, offering insights for further development in visual augmentation technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {Artful Expression, Artistic technique, Augmented Reality, Daily lives, Defamiliarization, Dynamic environments, Engineering education, Enhanced vision systems, Generative AI, generative artificial intelligence, Human augmentation, Human engineering, Human-AI Interaction, Human-artificial intelligence interaction, Semi-transparent},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{tortora_exploring_2025,

title = {Exploring the Role of Generative Artificial Intelligence in Virtual Reality: Opportunities and Future Perspectives},

author = {A. Tortora and I. Amaro and A. Della Greca and P. Barra},

editor = {Chen J.Y.C. and Fragomeni G.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007788684&doi=10.1007%2f978-3-031-93700-2_9&partnerID=40&md5=7b69183bbf8172f9595f939254fb6831},

doi = {10.1007/978-3-031-93700-2_9},

isbn = {03029743 (ISSN); 978-303193699-9 (ISBN)},

year  = {2025},

date = {2025-01-01},

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

volume = {15788 LNCS},

pages = {125–142},

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

abstract = {In recent years, generative AI, such as language and image models, have started to revolutionize virtual reality (VR) by offering new opportunities for immersive and personalized interaction. This paper explores the potential of these Intelligent Augmentation technologies in the context of VR, analyzing how the generation of text and images in real time can enhance the user experience through dynamic and personalized environments and contents. The integration of generative AI in VR scenarios holds promise in multiple fields, including education, professional training, design, and healthcare. However, their implementation involves significant challenges, such as privacy management, data security, and ethical issues related to cognitive manipulation and representation of reality. Through an overview of current applications and future prospects, this paper highlights the crucial role of generative AI in enhancing VR, helping to outline a path for the ethical and sustainable development of these immersive technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {Ethical technology, Future perspectives, Generative AI, Image modeling, Immersive, immersive experience, Immersive Experiences, Information Management, Language Model, Personnel training, Professional training, Real- time, Sensitive data, Training design, Users' experiences, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{monjoree_assessing_2025,

title = {Assessing AI Models' Spatial Visualization in PSVT:R and Augmented Reality: Towards Enhancing AI's Spatial Intelligence},

author = {U. Monjoree and W. Yan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011255775&doi=10.1109%2FCAI64502.2025.00131&partnerID=40&md5=0bd551863839b3025898e55265403969},

doi = {10.1109/CAI64502.2025.00131},

isbn = {9798331524005 (ISBN)},

year  = {2025},

date = {2025-01-01},

pages = {727–734},

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

abstract = {Spatial intelligence is important in many fields, such as Architecture, Engineering, and Construction (AEC), Science, Technology, Engineering, and Mathematics (STEM), and Medicine. Understanding three-dimensional (3D) spatial rotations can involve verbal descriptions and visual or interactive examples, illustrating how objects move and change orientation in 3D space. Recent studies show that artificial intelligence (AI) with language and vision capabilities still faces limitations in spatial reasoning. In this paper, we have studied the spatial capabilities of advanced generative AI to understand the rotations of objects in 3D space utilizing its image processing and language processing features. We examined the spatial intelligence of three generative AI models (GPT-4, Gemini 1.5 Pro, and Llama 3.2) to understand the spatial rotation process with spatial rotation diagrams based on the revised Purdue Spatial Visualization Test: Visualization of Rotations (Revised PSVT:R). Furthermore, we incorporated an added layer of a coordinate system axes on Revised PSVT:R to study the variations in generative AI models' performance. We additionally examined generative AI models' understanding of 3D rotations in Augmented Reality (AR) scene images that visualize spatial rotations of a physical object in 3D space and observed an increased accuracy of generative AI models' understanding of rotations by adding additional textual information depicting the rotation process or mathematical representations of the rotation (e.g., matrices) superimposed on the object. The results indicate that while GPT-4, Gemini 1.5 Pro, and Llama 3.2 as the main current generative AI model lack the understanding of a spatial rotation process, it has the potential to understand the rotation process with additional information that can be provided by methods such as AR. AR can superimpose textual information or mathematical representations of rotations on spatial transformation diagrams and create a more intelligible input for AI to comprehend or for training AI's spatial intelligence. Furthermore, by combining the potentials in spatial intelligence of AI with AR's interactive visualization abilities, we expect to offer enhanced guidance for students' spatial learning activities. Such spatial guidance can greatly benefit understanding spatial transformations and additionally support processes like assembly, construction, manufacturing, as well as learning in AEC, STEM, and Medicine that require precise 3D spatial understanding. © 2025 Elsevier B.V., All rights reserved.},

keywords = {3D modeling, Architecture engineering, Artificial intelligence, Augmented Reality, Construction science, Engineering education, Engineering science, Generative AI, generative artificial intelligence, Image processing, Intelligence models, Linear transformations, Medicine, Rotation, Rotation process, Spatial Intelligence, Spatial rotation, Spatial visualization, Three dimensional computer graphics, Three dimensional space, Visualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{basouli_application_2025,

title = {Application of Generative Artificial Intelligence in Simulating Virtual Tourism Experiences: Examining the Impact on Post-COVID Tourist Behavior},

author = {M. Basouli and S. Sheikhooni},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011597291&doi=10.1109%2FICWR65219.2025.11006234&partnerID=40&md5=55413d1f514a58726eed134828203915},

doi = {10.1109/ICWR65219.2025.11006234},

isbn = {28378296 (ISSN); 28378288 (ISSN)},

year  = {2025},

date = {2025-01-01},

pages = {593–596},

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

abstract = {This article examines the application of generative artificial intelligence in simulating virtual tourism experiences and its impact on tourist behavior in the postCOVID era. Utilizing advanced technologies such as Stable Diffusion, ChatGPT, and WebXR, a system has been designed to create interactive virtual experiences of tourist destinations. A literature review reveals that both virtual experiences and generative AI hold significant potential in the tourism industry. However, few studies have explored how these two technologies can be combined and their impact on tourist behavior. Additionally, considering that generative AI, as a tool for simulating tourism experiences, significantly influences tourists' perception of destinations and attractions, travel intention, travel anxiety, and willingness to pay, studying generative AI and virtual tourism seems essential and important. Therefore, this study aims to review previous research and explore the impact of AI-based virtual tourism experiences on tourist behavior in the post-COVID era. Moreover, the study investigates factors influencing the effectiveness of these experiences and the moderating role of safety and health concerns. Results also show that perceived realism and interactivity of virtual experiences are key factors in the effectiveness of these experiences. This research provides a theoretical framework for understanding the influence of generative AI on tourism behavior and offers important practical implications for destination marketers and policymakers in the post-COVID tourism industry. © 2025 Elsevier B.V., All rights reserved.},

note = {Issue: 2025},

keywords = {Advanced technology, Artificial intelligence, Behavioral Research, Commerce, Covid-19, Destination Marketing, Generative AI, Leisure industry, Literature reviews, Post-COVID, Tourism, Tourism industry, Tourist behavior, Tourist destinations, Virtual environments, Virtual Reality, Virtual Tourism, WebXR},

pubstate = {published},

tppubtype = {inproceedings}

}