AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Zhang, Z.; Jiang, Y.; Wei, X.; Chen, M.; Dong, H.; Yu, S.
Generative-AI for XR Content Transmission in the Metaverse: Potential Approaches, Challenges, and a Generation-Driven Transmission Framework Journal Article
In: IEEE Network, 2025, ISSN: 08908044 (ISSN).
Abstract | Links | BibTeX | Tags: '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
@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},
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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.
Linares-Pellicer, J.; Izquierdo-Domenech, J.; Ferri-Molla, I.; Aliaga-Torro, C.
Breaking the Bottleneck: Generative AI as the Solution for XR Content Creation in Education Book Section
In: Lecture Notes in Networks and Systems, vol. 1140, pp. 9–30, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 23673370 (ISSN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Augmented Reality, Breakings, Content creation, Contrastive Learning, Development process, Educational context, Federated learning, Generative adversarial networks, Immersive learning, Intelligence models, Learning experiences, Mixed reality, Resource intensity, Technical skills, Virtual environments
@incollection{linares-pellicer_breaking_2025,
title = {Breaking the Bottleneck: Generative AI as the Solution for XR Content Creation in Education},
author = {J. Linares-Pellicer and J. Izquierdo-Domenech and I. Ferri-Molla and C. Aliaga-Torro},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212478399&doi=10.1007%2f978-3-031-71530-3_2&partnerID=40&md5=aefee938cd5b8a74ee811a463d7409ae},
doi = {10.1007/978-3-031-71530-3_2},
isbn = {23673370 (ISSN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lecture Notes in Networks and Systems},
volume = {1140},
pages = {9–30},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The integration of Extended Reality (XR) technologies-Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)-promises to revolutionize education by offering immersive learning experiences. However, the complexity and resource intensity of content creation hinders the adoption of XR in educational contexts. This chapter explores Generative Artificial Intelligence (GenAI) as a solution, highlighting how GenAI models can facilitate the creation of educational XR content. GenAI enables educators to produce engaging XR experiences without needing advanced technical skills by automating aspects of the development process from ideation to deployment. Practical examples demonstrate GenAI’s current capability to generate assets and program applications, significantly lowering the barrier to creating personalized and interactive learning environments. The chapter also addresses challenges related to GenAI’s application in education, including technical limitations and ethical considerations. Ultimately, GenAI’s integration into XR content creation makes immersive educational experiences more accessible and practical, driven by only natural interactions, promising a future where technology-enhanced learning is universally attainable. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Adversarial machine learning, Augmented Reality, Breakings, Content creation, Contrastive Learning, Development process, Educational context, Federated learning, Generative adversarial networks, Immersive learning, Intelligence models, Learning experiences, Mixed reality, Resource intensity, Technical skills, Virtual environments},
pubstate = {published},
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The integration of Extended Reality (XR) technologies-Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)-promises to revolutionize education by offering immersive learning experiences. However, the complexity and resource intensity of content creation hinders the adoption of XR in educational contexts. This chapter explores Generative Artificial Intelligence (GenAI) as a solution, highlighting how GenAI models can facilitate the creation of educational XR content. GenAI enables educators to produce engaging XR experiences without needing advanced technical skills by automating aspects of the development process from ideation to deployment. Practical examples demonstrate GenAI’s current capability to generate assets and program applications, significantly lowering the barrier to creating personalized and interactive learning environments. The chapter also addresses challenges related to GenAI’s application in education, including technical limitations and ethical considerations. Ultimately, GenAI’s integration into XR content creation makes immersive educational experiences more accessible and practical, driven by only natural interactions, promising a future where technology-enhanced learning is universally attainable. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
2024
Lai, B.; He, J.; Kang, J.; Li, G.; Xu, M.; Zhang, T.; Xie, S.
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks Proceedings Article
In: M., Valenti; D., Reed; M., Torres (Ed.): IEEE Int Conf Commun, pp. 2883–2888, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 15503607 (ISSN); 978-172819054-9 (ISBN).
Abstract | Links | BibTeX | Tags: EDGE Networks, Energy Efficient, Federated Diffusion, Federated learning, Generative adversarial networks, Generative AI, Generative Diffusion, Intelligence models, Metaverses, On demands, On-demand Quantization, Quantisation
@inproceedings{lai_-demand_2024,
title = {On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks},
author = {B. Lai and J. He and J. Kang and G. Li and M. Xu and T. Zhang and S. Xie},
editor = {Valenti M. and Reed D. and Torres M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202836582&doi=10.1109%2fICC51166.2024.10622695&partnerID=40&md5=8318ef451bb946470eb34ef50d09b5a1},
doi = {10.1109/ICC51166.2024.10622695},
isbn = {15503607 (ISSN); 978-172819054-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Int Conf Commun},
pages = {2883–2888},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Generative Artificial Intelligence (GAI) shows remarkable productivity and creativity in Mobile Edge Networks, such as the metaverse and the Industrial Internet of Things. Federated learning is a promising technique for effectively training GAI models in mobile edge networks due to its data distribution. However, there is a notable issue with communication consumption when training large GAI models like generative diffusion models in mobile edge networks. Additionally, the substantial energy consumption associated with training diffusion-based models, along with the limited resources of edge devices and complexities of network environments, pose challenges for improving the training efficiency of GAI models. To address this challenge, we propose an on-demand quantized energy-efficient federated diffusion approach for mobile edge networks. Specifically, we first design a dynamic quantized federated diffusion training scheme considering various demands from the edge devices. Then, we study an energy efficiency problem based on specific quantization requirements. Numerical results show that our proposed method significantly reduces system energy consumption and transmitted model size compared to both baseline federated diffusion and fixed quantized federated diffusion methods while effectively maintaining reasonable quality and diversity of generated data. © 2024 IEEE.},
keywords = {EDGE Networks, Energy Efficient, Federated Diffusion, Federated learning, Generative adversarial networks, Generative AI, Generative Diffusion, Intelligence models, Metaverses, On demands, On-demand Quantization, Quantisation},
pubstate = {published},
tppubtype = {inproceedings}
}
Generative Artificial Intelligence (GAI) shows remarkable productivity and creativity in Mobile Edge Networks, such as the metaverse and the Industrial Internet of Things. Federated learning is a promising technique for effectively training GAI models in mobile edge networks due to its data distribution. However, there is a notable issue with communication consumption when training large GAI models like generative diffusion models in mobile edge networks. Additionally, the substantial energy consumption associated with training diffusion-based models, along with the limited resources of edge devices and complexities of network environments, pose challenges for improving the training efficiency of GAI models. To address this challenge, we propose an on-demand quantized energy-efficient federated diffusion approach for mobile edge networks. Specifically, we first design a dynamic quantized federated diffusion training scheme considering various demands from the edge devices. Then, we study an energy efficiency problem based on specific quantization requirements. Numerical results show that our proposed method significantly reduces system energy consumption and transmitted model size compared to both baseline federated diffusion and fixed quantized federated diffusion methods while effectively maintaining reasonable quality and diversity of generated data. © 2024 IEEE.
Weng, S. C. -C.; Chiou, Y. -M.; Do, E. Y. -L.
Dream Mesh: A Speech-to-3D Model Generative Pipeline in Mixed Reality Proceedings Article
In: Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR, pp. 345–349, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835037202-1 (ISBN).
Abstract | Links | BibTeX | Tags: 3D content, 3D modeling, 3D models, 3d-modeling, Augmented Reality, Digital assets, Generative AI, generative artificial intelligence, Intelligence models, Mesh generation, Mixed reality, Modeling, Speech-to-3D, Text modeling, Three dimensional computer graphics, User interfaces
@inproceedings{weng_dream_2024,
title = {Dream Mesh: A Speech-to-3D Model Generative Pipeline in Mixed Reality},
author = {S. C. -C. Weng and Y. -M. Chiou and E. Y. -L. Do},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187218106&doi=10.1109%2fAIxVR59861.2024.00059&partnerID=40&md5=5bfe206e841f23de6458f88a0824bd4d},
doi = {10.1109/AIxVR59861.2024.00059},
isbn = {979-835037202-1 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},
pages = {345–349},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Generative Artificial Intelligence (AI) models have risen to prominence due to their unparalleled ability to craft and generate digital assets, encompassing text, images, audio, video, and 3D models. Leveraging the capabilities of diffusion models, such as Stable Diffusion and Instruct pix2pix, users can guide AI with specific prompts, streamlining the creative journey for graphic designers. However, the primary application of these models has been to graphic content within desktop interfaces, prompting professionals in interior and architectural design to seek more tailored solutions for their daily operations. To bridge this gap, Augmented Reality (AR) and Mixed Reality (MR) technologies offer a promising solution, transforming traditional 2D artworks into engaging 3D interactive realms. In this paper, we present "Dream Mesh,"a MR application MR tool that combines a Speech-to-3D generative workflow besed on DreamFusion model without relying on pre-existing 3D content libraries. This innovative system empowers users to express 3D content needs through natural language input, promising transformative potential in real-time 3D content creation and an enhanced MR user experience. © 2024 IEEE.},
keywords = {3D content, 3D modeling, 3D models, 3d-modeling, Augmented Reality, Digital assets, Generative AI, generative artificial intelligence, Intelligence models, Mesh generation, Mixed reality, Modeling, Speech-to-3D, Text modeling, Three dimensional computer graphics, User interfaces},
pubstate = {published},
tppubtype = {inproceedings}
}
Generative Artificial Intelligence (AI) models have risen to prominence due to their unparalleled ability to craft and generate digital assets, encompassing text, images, audio, video, and 3D models. Leveraging the capabilities of diffusion models, such as Stable Diffusion and Instruct pix2pix, users can guide AI with specific prompts, streamlining the creative journey for graphic designers. However, the primary application of these models has been to graphic content within desktop interfaces, prompting professionals in interior and architectural design to seek more tailored solutions for their daily operations. To bridge this gap, Augmented Reality (AR) and Mixed Reality (MR) technologies offer a promising solution, transforming traditional 2D artworks into engaging 3D interactive realms. In this paper, we present "Dream Mesh,"a MR application MR tool that combines a Speech-to-3D generative workflow besed on DreamFusion model without relying on pre-existing 3D content libraries. This innovative system empowers users to express 3D content needs through natural language input, promising transformative potential in real-time 3D content creation and an enhanced MR user experience. © 2024 IEEE.
Schmidt, P.; Arlt, S.; Ruiz-Gonzalez, C.; Gu, X.; Rodríguez, C.; Krenn, M.
Virtual reality for understanding artificial-intelligence-driven scientific discovery with an application in quantum optics Journal Article
In: Machine Learning: Science and Technology, vol. 5, no. 3, 2024, ISSN: 26322153 (ISSN).
Abstract | Links | BibTeX | Tags: 3-dimensional, Analysis process, Digital discovery, Generative adversarial networks, Generative model, generative models, Human capability, Immersive virtual reality, Intelligence models, Quantum entanglement, Quantum optics, Scientific discovery, Scientific understanding, Virtual Reality, Virtual-reality environment
@article{schmidt_virtual_2024,
title = {Virtual reality for understanding artificial-intelligence-driven scientific discovery with an application in quantum optics},
author = {P. Schmidt and S. Arlt and C. Ruiz-Gonzalez and X. Gu and C. Rodríguez and M. Krenn},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201265211&doi=10.1088%2f2632-2153%2fad5fdb&partnerID=40&md5=3a6af280ba0ac81507ade10f5dd1efb3},
doi = {10.1088/2632-2153/ad5fdb},
issn = {26322153 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Machine Learning: Science and Technology},
volume = {5},
number = {3},
abstract = {Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive virtual reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way—as a human-in-the-loop—to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger-Horne-Zeilinger-state analyzer. Our results show the potential of VR to enhance a researcher’s ability to derive knowledge from graph-based generative AI. This type of AI is a widely used abstract data representation in various scientific fields. © 2024 The Author(s). Published by IOP Publishing Ltd.},
keywords = {3-dimensional, Analysis process, Digital discovery, Generative adversarial networks, Generative model, generative models, Human capability, Immersive virtual reality, Intelligence models, Quantum entanglement, Quantum optics, Scientific discovery, Scientific understanding, Virtual Reality, Virtual-reality environment},
pubstate = {published},
tppubtype = {article}
}
Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive virtual reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way—as a human-in-the-loop—to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger-Horne-Zeilinger-state analyzer. Our results show the potential of VR to enhance a researcher’s ability to derive knowledge from graph-based generative AI. This type of AI is a widely used abstract data representation in various scientific fields. © 2024 The Author(s). Published by IOP Publishing Ltd.