Publications

@article{stroinski_text–haptics_2025,

title = {Text-to-Haptics: Enhancing Multisensory Storytelling through Emotionally Congruent Midair Haptics},

author = {M. Stroinski and K. Kwarciak and M. Kowalewski and D. Hemmerling and W. Frier and O. Georgiou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002269591&doi=10.1002%2faisy.202400758&partnerID=40&md5=a4c8ce7a01c9bc90d9805a81d34df982},

doi = {10.1002/aisy.202400758},

issn = {26404567 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Advanced Intelligent Systems},

volume = {7},

number = {4},

abstract = {In multisensory storytelling, the integration of touch, sound, speech, and visual elements plays a crucial role in enhancing the narrative immersion and audience engagement. In light of this, this article presents a scalable and intelligent hybrid artificial intelligence (AI) method that uses emotional text analysis for deciding when and what midair haptics to display alongside audiovisual content generated by latent stable diffusion methods. Then, a user study involving 40 participants is described, the results of which suggest that the proposed approach enhances the audience level of engagement as they experience a short AI-generated multisensory (audio–visual–haptic) story. © 2024 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH.},

keywords = {Audiovisual, Augmented Reality, Extended reality, Haptic interfaces, Haptics, Haptics interfaces, HMI, hybrid AI, Hybrid artificial intelligences, Metaverses, Mixed reality, Multisensory, Natural Language Processing, perception, Sentiment Analysis, Sound speech, Special issue and section, Speech enhancement, Virtual environments, Visual elements},

pubstate = {published},

tppubtype = {article}

}

@article{liu_contract-inspired_2025,

title = {Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse},

author = {G. Liu and H. Du and J. Wang and D. Niyato and D. I. Kim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000066834&doi=10.1109%2FTMC.2025.3550815&partnerID=40&md5=f95abb0df00e3112fa2c15ee77eb41bc},

doi = {10.1109/TMC.2025.3550815},

issn = {15361233 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Transactions on Mobile Computing},

volume = {24},

number = {8},

pages = {7389–7405},

abstract = {The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},

keywords = {Contest Theory, Deep learning, Deep reinforcement learning, Diffusion Model, Generative adversarial networks, Generative AI, High quality, Image generation, Image generations, Immersive technologies, Metaverses, Mobile edge computing, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Semantic data, Virtual addresses, Virtual environments, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{aloudat_metaverse_2025,

title = {Metaverse Unbound: A Survey on Synergistic Integration Between Semantic Communication, 6G, and Edge Learning},

author = {M. Z. Aloudat and A. Aboumadi and A. Soliman and H. A. Al-Mohammed and M. al-Ali and A. Mahgoub and M. Barhamgi and E. Yaacoub},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003088610&doi=10.1109%2FACCESS.2025.3555753&partnerID=40&md5=c84a85efab6a29ee6916f5698922f720},

doi = {10.1109/ACCESS.2025.3555753},

issn = {21693536 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Access},

volume = {13},

pages = {58302–58350},

abstract = {With a focus on edge learning, blockchain, sixth generation (6G) wireless systems, semantic communication, and large language models (LLMs), this survey paper examines the revolutionary integration of cutting-edge technologies within the metaverse. This thorough examination highlights the critical role these technologies play in improving realism and user engagement on three main levels: technical, virtual, and physical. While the virtual layer focuses on building immersive experiences, the physical layer highlights improvements to the user interface through augmented reality (AR) goggles and virtual reality (VR) headsets. Blockchain-powered technical layer enables safe, decentralized communication. The survey highlights how the metaverse has the potential to drastically change how people interact in society by exploring applications in a variety of fields, such as immersive education, remote work, and entertainment. Concerns about privacy, scalability, and interoperability are raised, highlighting the necessity of continued study to realize the full potential of the metaverse. For scholars looking to broaden the reach and significance of the metaverse in the digital age, this paper is a useful tool. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},

keywords = {6g wireless system, 6G wireless systems, Augmented Reality, Block-chain, Blockchain, Blockchain technology, Digital Twin Technology, Edge learning, Extended reality (XR), Language Model, Large language model, large language models (LLMs), Metaverse, Metaverses, Semantic communication, Virtual environments, Wireless systems},

pubstate = {published},

tppubtype = {article}

}

@article{qian_iot_2025,

title = {IoT in Sustainability and IoT in the AI and Metaverse Age},

author = {Y. Qian and K. L. Siau},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004261858&doi=10.1109%2FIOTM.001.2400179&partnerID=40&md5=45fa3ed982a2b82ddd18058822700fff},

doi = {10.1109/IOTM.001.2400179},

issn = {25763199 (ISSN); 25763180 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Internet of Things Magazine},

volume = {8},

number = {3},

pages = {92–98},

abstract = {The Internet of Things (IoT) is a modern technology that has gained large popularity and is still developing. Connecting heterogeneous devices, such as phones, vehicles, and household appliances, IoT has brought convenience to our lives. Further, IoT plays a significant role in enhancing environmental sustainability. It provides timely data about different devices and enables users and managers to directly control the objects. IoT can optimize the existing energy systems and promote the usage of renewable technologies. In this article, we discuss how IoT supports green initiatives (i.e., how it is applied in different sectors), how it can be “green” itself (i.e., from both the technical and managerial perspectives), and future directions of the advancement of IoT (e.g., IoT in the AI and Metaverse age). Specifically, we explain how IoT enhances environmental sustainability in different sectors, including energy, agriculture, smart cities, and transportation. We also show how IoT can be green itself through technical and managerial approaches. Further, we discuss how IoT can be integrated with AI, such as Generative AI, Agentic AI, and the Metaverse, to enhance impacts. Clearly, IoT has the potential to be further developed and make an even greater contribution to environmental sustainability. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},

keywords = {Energy, Energy systems, Environmental sustainability, Existing energies, Heterogeneous devices, Metaverses, Modern technologies, Renewable technology},

pubstate = {published},

tppubtype = {article}

}

@article{cao_unified_2025,

title = {A Unified Framework for Underwater Metaverse with Optical Perception},

author = {J. Cao and M. Zhou and J. Wang and G. Liu and D. Niyato and S. Mao and Z. Han and J. Kang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105006878504&doi=10.1109%2FMWC.006.2400050&partnerID=40&md5=6091264c581faa5f1349483d889ebb3e},

doi = {10.1109/MWC.006.2400050},

issn = {15361284 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Wireless Communications},

volume = {32},

number = {3},

pages = {220–231},

abstract = {With the advancement of AI technology and increasing attention to deep-sea exploration, the underwater Metaverse is gradually emerging. This article explores the concept of underwater Metaverse, emerging virtual reality systems, and services aimed at simulating and enhancing the virtual experience of marine environments. First, we discuss potential applications of underwater Metaverse in underwater scientific research and marine conservation. Next, we design the architecture and highlight the corresponding design requirements. Then, we present the characteristics of different underwater imaging technologies, such as underwater acoustic imaging, underwater radio imaging, and underwater quantum imaging, in the supporting technologies of the underwater Metaverse. Quantum imaging (QI) technology is suitable for extremely low-light underwater environments, improving the precision and efficiency of underwater imaging. Based on this, we present a use case for building a realistic underwater virtual world using underwater quantum imaging-generative artificial intelligence (QI-GenAI) technology. The results demonstrate the effectiveness of the underwater Metaverse framework in simulating complex underwater environments, thus validating its potential to provide high-quality, interactive underwater virtual experiences. Finally, the article examines important future research directions of underwater Metaverse and provides new perspectives for marine science and conservation. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},

keywords = {AI Technologies, Deep sea exploration, Imaging technology, Marine conservations, Metaverses, Optical-, Quantum imaging, Underwater environments, Unified framework, Virtual reality system},

pubstate = {published},

tppubtype = {article}

}

@article{kurai_magiccraft_2025,

title = {MagicCraft: Natural Language-Driven Generation of Dynamic and Interactive 3D Objects 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-s2.0-105010187256&doi=10.1109%2FACCESS.2025.3587232&partnerID=40&md5=9b7a8115c62a8f9da4956dbbbb53dc4e},

doi = {10.1109/ACCESS.2025.3587232},

issn = {21693536 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Access},

volume = {13},

pages = {132459–132474},

abstract = {Metaverse platforms are rapidly evolving to provide immersive spaces for user interaction and content creation. However, the generation of dynamic and interactive 3D objects remains challenging due to the need for advanced 3D modeling and programming skills. To address this challenge, we present MagicCraft, a system that generates functional 3D objects from natural language prompts for metaverse platforms. 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 such as orientation, scale, seating positions, and grip points. Implemented on Cluster, a commercial metaverse platform, MagicCraft was evaluated by 7 expert CG designers and 51 general users. Results show that MagicCraft significantly reduces the time and skill required to create 3D objects. Users with no prior experience in 3D modeling or programming successfully created complex, interactive objects and deployed them in the metaverse. Expert feedback highlighted the system's potential to improve content creation workflows and support rapid prototyping. By integrating AI-generated content into metaverse platforms, MagicCraft makes 3D content creation more accessible. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},

keywords = {3D models, 3D object, 3D Object Generation, 3d-modeling, AI-Assisted Design, Artificial intelligence, Behavioral Research, Content creation, Generative AI, Immersive, Metaverse, Metaverses, Natural language processing systems, Natural languages, Object oriented programming, Three dimensional computer graphics, user experience, User interfaces},

pubstate = {published},

tppubtype = {article}

}

@article{richter_narratives_2025,

title = {Narratives of the Future-Exploring Human-AI Collaboration},

author = {S. Richter and A. Richter and M. Trier},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011937728&partnerID=40&md5=28f6b90199fcfc1044fc27d74a20bdc4},

issn = {15293181 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Communications of the Association for Information Systems},

volume = {57},

abstract = {As organizational leaders and employees navigate the new landscape of hybrid work, two additional technological innovations are quickly becoming prevalent: Human-AI Collaboration and new forms of presence in virtual environments such as the Metaverse. While both developments are still in the early stages, the example of generative AI has shown how fast emerging technology can have important implications for organizational practices. But how do we study something that has not yet materialized? In this paper, we apply web-based horizon scanning and develop four narratives, based on tasks in McGrath's group task circumplex, to engage with the future of work. Beyond dis-cussing the possible implications of the narratives and providing exemplary research questions, we explore and discuss horizon scanning as a future-looking methodology for information systems re-search. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Association for Information Systems},

keywords = {Emerging technologies, Future, Horizon Scanning, Human engineering, Human-ai collaboration, Hybrid Work, Information Systems, Information use, Metaverse, Metaverses, Narrative, Narratives, New forms, Organisational, Technological innovation, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{basyoni_generative_2025,

title = {Generative AI-Driven Metaverse: The Promises and Challenges of AI-Generated Content},

author = {L. Basyoni and A. Qayyum and K. Bashir Shaban and E. Elmahjub and A. Al-Ali and O. Halabi and J. Qadir},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105018067689&doi=10.1109%2FOJCS.2025.3616501&partnerID=40&md5=1c24838c13edfdb187842c663121483d},

doi = {10.1109/OJCS.2025.3616501},

issn = {26441268 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Open Journal of the Computer Society},

abstract = {Recent advancements in Artificial Intelligence (AI) and immersive technologies, such as Extended Reality (XR), coupled with complementary innovations like 5G/6G wireless communications, are paving the way for fully realized AI-XR metaverses. AI will play a pivotal role in this transformation, enabling the seamless convergence of virtual and physical worlds. Among the various AI applications, Generative AI (GenAI) stands out for creating rich, dynamic, and interactive virtual environments—essential elements for sustained metaverse growth and user engagement. To facilitate a deeper understanding of how GenAI will be integrated and utilized within the metaverse, we provide a comprehensive overview of GenAI and Artificial Intelligence-Generated Content (AIGC). Specifically, we examine the potential influence of GenAI's on future metaverses, exploring both the opportunities it offers and the major challenges associated with its deployment. Additionally, we investigate the robustness of AIGC detection techniques against adversarial attacks, highlighting less-explored risks posed by adversarial examples. Finally, we highlight various open research issues related to GenAI, AIGC, the metaverse, and responsible innovation that merit further exploration. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},

keywords = {Artificial intelligence technologies, Dynamic virtual environment, Essential elements, Immersive technologies, Interactive virtual environments, Metaverses, Physical world, Rich dynamics, Virtual environments, Virtual Reality, Virtual worlds, Wireless communications},

pubstate = {published},

tppubtype = {article}

}

@article{huang_privacy_2025,

title = {Privacy Preservation of Large Language Models in the Metaverse Era: Research Frontiers, Categorical Comparisons, and Future Directions},

author = {D. Huang and M. Ge and K. Xiang and X. Zhang and H. Yang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199980257&doi=10.1002%2Fnem.2292&partnerID=40&md5=55662aeedfb216784f0ed398cf8bd2f0},

doi = {10.1002/nem.2292},

issn = {10557148 (ISSN); 10991190 (ISSN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Int J Network Manage},

journal = {International Journal of Network Management},

volume = {35},

number = {1},

publisher = {John Wiley and Sons Ltd},

abstract = {Large language models (LLMs), with their billions to trillions of parameters, excel in natural language processing, machine translation, dialog systems, and text summarization. These capabilities are increasingly pivotal in the metaverse, where they can enhance virtual interactions and environments. However, their extensive use, particularly in the metaverse's immersive platforms, raises significant privacy concerns. This paper analyzes existing privacy issues in LLMs, vital for both traditional and metaverse applications, and examines protection techniques across the entire life cycle of these models, from training to user deployment. We delve into cryptography, embedding layer encoding, differential privacy and its variants, and adversarial networks, highlighting their relevance in the metaverse context. Specifically, we explore technologies like homomorphic encryption and secure multiparty computation, which are essential for metaverse security. Our discussion on Gaussian differential privacy, Renyi differential privacy, Edgeworth accounting, and the generation of adversarial samples and loss functions emphasizes their importance in the metaverse's dynamic and interactive environments. Lastly, the paper discusses the current research status and future challenges in the security of LLMs within and beyond the metaverse, emphasizing urgent problems and potential areas for exploration. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: John Wiley and Sons Ltd},

keywords = {Adversarial networks, Computational Linguistics, Cryptography, Differential privacies, Excel, Language Model, Large language model, large language models, Life cycle, Metaverse, Metaverses, Natural language processing systems, Natural languages, Privacy preservation, Privacy protection, Research frontiers},

pubstate = {published},

tppubtype = {article}

}

@article{kurai_magicitem_2025,

title = {MagicItem: Dynamic Behavior Design of Virtual Objects With Large Language Models in a Commercial Metaverse Platform},

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-s2.0-85216011970&doi=10.1109%2FACCESS.2025.3530439&partnerID=40&md5=6de2e69c95854cb0860a95d0f4246d8d},

doi = {10.1109/ACCESS.2025.3530439},

issn = {21693536 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Access},

volume = {13},

pages = {19132–19143},

abstract = {To create rich experiences in virtual reality (VR) environments, it is essential to define the behavior of virtual objects through programming. However, programming in 3D spaces requires a wide range of background knowledge and programming skills. Although Large Language Models (LLMs) have provided programming support, they are still primarily aimed at programmers. In metaverse platforms, where many users inhabit VR spaces, most users are unfamiliar with programming, making it difficult for them to modify the behavior of objects in the VR environment easily. Existing LLM-based script generation methods for VR spaces require multiple lengthy iterations to implement the desired behaviors and are difficult to integrate into the operation of metaverse platforms. To address this issue, we propose a tool that generates behaviors for objects in VR spaces from natural language within Cluster, a metaverse platform with a large user base. By integrating LLMs with the Cluster Script provided by this platform, we enable users with limited programming experience to define object behaviors within the platform freely. We have also integrated our tool into a commercial metaverse platform and are conducting online experiments with 63 general users of the platform. The experiments show that even users with no programming background can successfully generate behaviors for objects in VR spaces, resulting in a highly satisfying system. Our research contributes to democratizing VR content creation by enabling non-programmers to design dynamic behaviors for virtual objects in metaverse platforms. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},

keywords = {Behavior design, Code programming, Computer simulation languages, Dynamic behaviors, Language Model, Large-language model, Low-code programming, Metaverse platform, Metaverses, Virtual addresses, Virtual environments, Virtual objects, Virtual Reality, Virtual-reality environment},

pubstate = {published},

tppubtype = {article}

}

@article{li_refined_2025,

title = {Refined dense face alignment through image matching},

author = {C. Li and F. Da},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187924785&doi=10.1007%2Fs00371-024-03316-3&partnerID=40&md5=2de9f0dbdf9ea162871458c08e711c94},

doi = {10.1007/s00371-024-03316-3},

issn = {01782789 (ISSN); 14322315 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Visual Computer},

volume = {41},

number = {1},

pages = {157–171},

abstract = {Face alignment is the foundation of building 3D avatars for virtue communication in the metaverse, human-computer interaction, AI-generated content, etc., and therefore, it is critical that face deformation is reflected precisely to better convey expression, pose and identity. However, misalignment exists in the currently best methods that fit a face model to a target image and can be easily captured by human perception, thus degrading the reconstruction quality. The main reason is that the widely used metrics for training, including the landmark re-projection loss, pixel-wise loss and perception-level loss, are insufficient to address the misalignment and suffer from ambiguity and local minimums. To address misalignment, we propose an image MAtchinG-driveN dEnse geomeTrIC supervision (MAGNETIC). Specifically, we treat face alignment as a matching problem and establish pixel-wise correspondences between the target and rendered images. Then reconstructed facial points are guided towards their corresponding points on the target image, thus improving reconstruction. Synthesized image pairs are mixed up with face outliers to simulate the target and rendered images with ground-truth pixel-wise correspondences to enable the training of a robust prediction network. Compared with existing methods that turn to 3D scans for dense geometric supervision, our method reaches comparable shape reconstruction results with much lower effort. Experimental results on the NoW testset show that we reach the state-of-the-art among all self-supervised methods and even outperform methods using photo-realistic images. We also achieve comparable results with the state-of-the-art on the benchmark of Feng et al. Codes will be available at: github.com/ChunLLee/ReconstructionFromMatching. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Springer Science and Business Media Deutschland GmbH},

keywords = {3D Avatars, Alignment, Dense geometric supervision, Face alignment, Face deformations, Face reconstruction, Geometry, Human computer interaction, Image enhancement, Image matching, Image Reconstruction, Metaverses, Outlier mixup, Pixels, Rendered images, Rendering (computer graphics), State of the art, Statistics, Target images, Three dimensional computer graphics},

pubstate = {published},

tppubtype = {article}

}

@article{arai_digital_2025,

title = {Digital Twin Model from Freehanded Sketch to Facade Design, 2D-3D Conversion for Volume Design},

author = {K. Arai},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216872163&doi=10.14569%2FIJACSA.2025.0160109&partnerID=40&md5=9f746443c9b3f48b569308c73928c8b0},

doi = {10.14569/IJACSA.2025.0160109},

issn = {21565570 (ISSN); 2158107X (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {International Journal of Advanced Computer Science and Applications},

volume = {16},

number = {1},

pages = {88–95},

abstract = {The article proposes a method for creating digital twins from freehand sketches for facade design, converting 2D designs to 3D volumes, and integrating these designs into real-world GIS systems. It outlines a process that involves generating 2D exterior images from sketches using generative AI (Gemini 1.5 Pro), converting these 2D images into 3D models with TriPo, and creating design drawings with SketchUp. Additionally, it describes a method for creating 3D exterior images using GauGAN, all for the purpose of construction exterior evaluation. The paper also discusses generating BIM data using generative AI, converting BIM data (in IFC file format) to GeoTiff, and displaying this information in GIS using QGIS software. Moreover, it suggests a method for generating digital twins with SketchUp to facilitate digital design information sharing and simulation within a virtual space. Lastly, it advocates for a cost-effective AI system designed for small and medium-sized construction companies, which often struggle to adopt BIM, to harness the advantages of digital twins. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Science and Information Organization},

keywords = {2D/3D conversion, AI, Architectural design, BIM, Digital Twins, Facade design, Facades, GauGAN, Generative AI, GeoTiff, GIS, IFC format, Metaverse, Metaverses, SketchUp, TriPo, Volume design, Volume Rendering},

pubstate = {published},

tppubtype = {article}

}

@article{mekki_medical_2025,

title = {Medical Education Metaverses (MedEd Metaverses): Opportunities, Use Case, and Guidelines},

author = {Y. M. Mekki and L. V. Simon and W. D. Freeman and J. Qadir},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218631349&doi=10.1109%2FMC.2024.3474033&partnerID=40&md5=c15b15e28928b0f709e2b33b26098c74},

doi = {10.1109/MC.2024.3474033},

issn = {00189162 (ISSN); 15580814 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Computer},

volume = {58},

number = {3},

pages = {60–70},

abstract = {This article explores how artificial intelligence (AI), particularly generative AI (GenAI), can enhance extended reality (XR) applications in medical education (MedEd) metaverses. We compare traditional augmented reality/virtual reality methods with AI-enabled XR metaverses, highlighting improvements in immersive learning, adaptive feedback, personalized performance tracking, remote training, and resource efficiency. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: IEEE Computer Society},

keywords = {Adaptive feedback, Augmented Reality, Immersive learning, Medical education, Metaverses, Performance tracking, Remote resources, Remote training, Resource efficiencies, Training efficiency, Virtual environments},

pubstate = {published},

tppubtype = {article}

}

@article{zhang_diffusion-based_2025,

title = {Diffusion-Based Reinforcement Learning for Cooperative Offloading and Resource Allocation in Multi-UAV Assisted Edge-Enabled Metaverse},

author = {Z. Zhang and J. Wang and J. Chen and H. Fu and Z. Tong and C. Jiang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219108203&doi=10.1109%2FTVT.2025.3544879&partnerID=40&md5=b620573362df88291ef0bc7edc16247f},

doi = {10.1109/TVT.2025.3544879},

issn = {00189545 (ISSN); 19399359 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Transactions on Vehicular Technology},

volume = {74},

number = {7},

pages = {11281–11293},

abstract = {As one of the typical applications of 6G, the metaverse, with its superior immersion and diversified services, has garnered widespread attention from both the global industry and academia. Simultaneously, the emergence of AI-generated content (AIGC), exemplified by ChatGPT, has revolutionized the means of content creation in the metaverse. Providing metaverse users with diversified AIGC services anytime and anywhere to meet the demand for immersive and blended virtual-real experiences in the physical world has become a major challenge in the development of the metaverse. Considering the flexibility and mobility of uncrewed aerial vehicles (UAVs), we innovatively incorporate multiple UAVs as one of the AIGC service providers and construct a multi-UAV assisted edge-enabled metaverse system in the context of AIGC-as-a-Service (AaaS) scenario. To solve the complex resource management and allocation problem in the aforementioned system, we formulate it as a Markov decision process (MDP) and propose utilizing the generative capabilities of the diffusion model in combination with the robust decision-making abilities of reinforcement learning to tackle these issues. In order to substantiate the efficacy of the proposed diffusion-based reinforcement learning framework, we propose a novel diffusion-based soft actor-critic algorithm for metaverse (Meta-DSAC). Subsequently, a series of experiments are executed and the simulation results empirically validate the proposed algorithm's comparative advantages of the ability to provide stable and substantial long-term rewards, as well as the enhanced capacity to model complex environments. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},

keywords = {Aerial vehicle, Content creation, Content services, Contrastive Learning, Decision making, Deep learning, Deep reinforcement learning, Diffusion Model, Global industry, Helicopter services, Markov processes, Metaverse, Metaverses, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Typical application, uncrewedaerial vehicle (UAV), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},

pubstate = {published},

tppubtype = {article}

}

@article{zhang_generative-ai_2025,

title = {Generative-AI for XR Content Transmission in the Metaverse: Potential Approaches, Challenges, and a Generation-Driven Transmission Framework},

author = {Z. Zhang and Y. Jiang and X. Wei and M. Chen and H. Dong and S. Yu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-86000661262&doi=10.1109%2fMNET.2025.3547385&partnerID=40&md5=1e00d40542ec58ef1489934abb2a990c},

doi = {10.1109/MNET.2025.3547385},

issn = {08908044 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Network},

abstract = {How to efficiently transmit large volumes of Extended Reality (XR) content through current networks has been a major bottleneck in realizing the Metaverse. The recently emerging Generative Artificial Intelligence (GAI) has already revolutionized various technological fields and provides promising solutions to this challenge. In this article, we first demonstrate current networks' bottlenecks for supporting XR content transmission in the Metaverse. Then, we explore the potential approaches and challenges of utilizing GAI to overcome these bottlenecks. To address these challenges, we propose a GAI-based XR content transmission framework which leverages a cloud-edge collaboration architecture. The cloud servers are responsible for storing and rendering the original XR content, while edge servers utilize GAI models to generate essential parts of XR content (e.g., subsequent frames, selected objects, etc.) when network resources are insufficient to transmit them. A Deep Reinforcement Learning (DRL)-based decision module is proposed to solve the decision-making problems. Our case study demonstrates that the proposed GAI-based transmission framework achieves a 2.8-fold increase in normal frame ratio (percentage of frames that meet the quality and latency requirements for XR content transmission) over baseline approaches, underscoring the potential of GAI models to facilitate XR content transmission in the Metaverse. © 2025 IEEE.},

keywords = {'current, Cloud servers, Collaboration architecture, Content transmission, Decision making, Deep learning, Deep reinforcement learning, Edge server, Generative adversarial networks, Intelligence models, Large volumes, Metaverses, Network bottlenecks, Reinforcement Learning, Through current},

pubstate = {published},

tppubtype = {article}

}