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 use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Yang, T.; Zhang, P.; Zheng, M.; Li, N.; Ma, S.
EnvReconGPT: A Generative AI Model for Wireless Environment Reconstruction in the 6G Metaverse Proceedings Article
In: Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331543709 (ISBN).
Abstract | Links | BibTeX | Tags: Environment reconstruction, Generative model, Generative pretrained transformer, High-fidelity, Imaging problems, Integrated sensing, Integrated sensing and communication, Metaverses, Mobile telecommunication systems, Signal processing, Three dimensional computer graphics, Wireless environment, Wireless environment reconstruction
@inproceedings{yang_envrecongpt_2025,
title = {EnvReconGPT: A Generative AI Model for Wireless Environment Reconstruction in the 6G Metaverse},
author = {T. Yang and P. Zhang and M. Zheng and N. Li and S. Ma},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017962023&doi=10.1109%2FINFOCOMWKSHPS65812.2025.11152849&partnerID=40&md5=3d776e0774732f91291ba6ff90078957},
doi = {10.1109/INFOCOMWKSHPS65812.2025.11152849},
isbn = {9798331543709 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This study introduces EnvReconGPT, a Transformer-based generative model specifically designed for wireless environment reconstruction in the 6G Metaverse, with a parameter size of 3.5 billion. The model leverages the generative capabilities of Transformers to frame wireless environment reconstruction as an imaging problem, treating it as a task of generating high-fidelity 3D point clouds. A novel multimodal mechanism is proposed to fuse base station positional information with Channel Frequency Response data, enabling the model to fully capture the spatial and spectral characteristics of the environment. Additionally, a feature embedding method is developed to integrate these multimodal inputs into the Transformer architecture effectively. By employing the Chamfer Distance as the loss function, EnvReconGPT ensures precise geometric alignment between predicted and ground truth point clouds, achieving robust performance across diverse scenarios. This work highlights the potential of generative AI in advancing ISAC systems and enabling wireless systems to meet the demands of the 6G Metaverse. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Environment reconstruction, Generative model, Generative pretrained transformer, High-fidelity, Imaging problems, Integrated sensing, Integrated sensing and communication, Metaverses, Mobile telecommunication systems, Signal processing, Three dimensional computer graphics, Wireless environment, Wireless environment reconstruction},
pubstate = {published},
tppubtype = {inproceedings}
}
This study introduces EnvReconGPT, a Transformer-based generative model specifically designed for wireless environment reconstruction in the 6G Metaverse, with a parameter size of 3.5 billion. The model leverages the generative capabilities of Transformers to frame wireless environment reconstruction as an imaging problem, treating it as a task of generating high-fidelity 3D point clouds. A novel multimodal mechanism is proposed to fuse base station positional information with Channel Frequency Response data, enabling the model to fully capture the spatial and spectral characteristics of the environment. Additionally, a feature embedding method is developed to integrate these multimodal inputs into the Transformer architecture effectively. By employing the Chamfer Distance as the loss function, EnvReconGPT ensures precise geometric alignment between predicted and ground truth point clouds, achieving robust performance across diverse scenarios. This work highlights the potential of generative AI in advancing ISAC systems and enabling wireless systems to meet the demands of the 6G Metaverse. © 2025 Elsevier B.V., All rights reserved.
Zhang, H.; Chen, P.; Xie, X.; Jiang, Z.; Wu, Y.; Li, Z.; Chen, X.; Sun, L.
FusionProtor: A Mixed-Prototype Tool for Component-level Physical-to-Virtual 3D Transition and Simulation Proceedings Article
In: Conf Hum Fact Comput Syst Proc, Association for Computing Machinery, 2025, ISBN: 9798400713958 (ISBN); 9798400713941 (ISBN).
Abstract | Links | BibTeX | Tags: 3D modeling, 3D prototype, 3D simulations, 3d transition, Component levels, Conceptual design, Creatives, Generative AI, High-fidelity, Integrated circuit layout, Mixed reality, Product conceptual designs, Prototype tools, Prototype workflow, Three dimensional computer graphics, Usability engineering, Virtual Prototyping
@inproceedings{zhang_fusionprotor_2025,
title = {FusionProtor: A Mixed-Prototype Tool for Component-level Physical-to-Virtual 3D Transition and Simulation},
author = {H. Zhang and P. Chen and X. Xie and Z. Jiang and Y. Wu and Z. Li and X. Chen and L. Sun},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005745450&doi=10.1145%2F3706598.3713686&partnerID=40&md5=a9f1229cb030c0af54842425f8d5c894},
doi = {10.1145/3706598.3713686},
isbn = {9798400713958 (ISBN); 9798400713941 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf Hum Fact Comput Syst Proc},
publisher = {Association for Computing Machinery},
abstract = {Developing and simulating 3D prototypes is crucial in product conceptual design for ideation and presentation. Traditional methods often keep physical and virtual prototypes separate, leading to a disjointed prototype workflow. In addition, acquiring high-fidelity prototypes is time-consuming and resource-intensive, distracting designers from creative exploration. Recent advancements in generative artificial intelligence (GAI) and extended reality (XR) provided new solutions for rapid prototype transition and mixed simulation. We conducted a formative study to understand current challenges in the traditional prototype process and explore how to effectively utilize GAI and XR ability in prototype. Then we introduced FusionProtor, a mixed-prototype tool for component-level 3D prototype transition and simulation. We proposed a step-by-step generation pipeline in FusionProtor, effectively transiting 3D prototypes from physical to virtual and low- to high-fidelity for rapid ideation and iteration. We also innovated a component-level 3D creation method and applied it in XR environment for the mixed-prototype presentation and interaction. We conducted technical and user experiments to verify FusionProtor's usability in supporting diverse designs. Our results verified that it achieved a seamless workflow between physical and virtual domains, enhancing efficiency and promoting ideation. We also explored the effect of mixed interaction on design and critically discussed its best practices for HCI community. © 2025 Elsevier B.V., All rights reserved.},
keywords = {3D modeling, 3D prototype, 3D simulations, 3d transition, Component levels, Conceptual design, Creatives, Generative AI, High-fidelity, Integrated circuit layout, Mixed reality, Product conceptual designs, Prototype tools, Prototype workflow, Three dimensional computer graphics, Usability engineering, Virtual Prototyping},
pubstate = {published},
tppubtype = {inproceedings}
}
Developing and simulating 3D prototypes is crucial in product conceptual design for ideation and presentation. Traditional methods often keep physical and virtual prototypes separate, leading to a disjointed prototype workflow. In addition, acquiring high-fidelity prototypes is time-consuming and resource-intensive, distracting designers from creative exploration. Recent advancements in generative artificial intelligence (GAI) and extended reality (XR) provided new solutions for rapid prototype transition and mixed simulation. We conducted a formative study to understand current challenges in the traditional prototype process and explore how to effectively utilize GAI and XR ability in prototype. Then we introduced FusionProtor, a mixed-prototype tool for component-level 3D prototype transition and simulation. We proposed a step-by-step generation pipeline in FusionProtor, effectively transiting 3D prototypes from physical to virtual and low- to high-fidelity for rapid ideation and iteration. We also innovated a component-level 3D creation method and applied it in XR environment for the mixed-prototype presentation and interaction. We conducted technical and user experiments to verify FusionProtor's usability in supporting diverse designs. Our results verified that it achieved a seamless workflow between physical and virtual domains, enhancing efficiency and promoting ideation. We also explored the effect of mixed interaction on design and critically discussed its best practices for HCI community. © 2025 Elsevier B.V., All rights reserved.
Tian, Y.; Li, X.; Cheng, Z.; Huang, Y.; Yu, T.
In: Sensors, vol. 25, no. 15, 2025, ISSN: 14248220 (ISSN), (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).
Abstract | Links | BibTeX | Tags: 3D faces, 3d facial model, 3D facial models, 3D modeling, adaptation, adult, Article, Audience perception evaluation, benchmarking, controlled study, Cross-modal, Face generation, Facial modeling, facies, Feature extraction, feedback, feedback system, female, Geometry, High-fidelity, human, illumination, Immersive media, Lighting, male, movie, Neural radiance field, Neural Radiance Fields, perception, Quality control, Rendering (computer graphics), Semantics, sensor, Three dimensional computer graphics, Virtual production, Virtual Reality
@article{tian_design_2025,
title = {Design of Realistic and Artistically Expressive 3D Facial Models for Film AIGC: A Cross-Modal Framework Integrating Audience Perception Evaluation},
author = {Y. Tian and X. Li and Z. Cheng and Y. Huang and T. Yu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105013137724&doi=10.3390%2Fs25154646&partnerID=40&md5=8508a27b693f0857ce7cb58e97a2705c},
doi = {10.3390/s25154646},
issn = {14248220 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Sensors},
volume = {25},
number = {15},
abstract = {The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this study proposes a cross-modal 3D face generation framework based on single-view semantic masks. It utilizes Swin Transformer for multi-level feature extraction and combines with NeRF for illumination decoupled rendering. We utilize physical rendering equations to explicitly separate surface reflectance from ambient lighting to achieve robust adaptation to complex lighting variations. In addition, to address geometric errors across illumination scenes, we construct geometric a priori constraint networks by mapping 2D facial features to 3D parameter space as regular terms with the help of semantic masks. On the CelebAMask-HQ dataset, this method achieves a leading score of SSIM = 0.892 (37.6% improvement from baseline) with FID = 40.6. The generated faces excel in symmetry and detail fidelity with realism and aesthetic scores of 8/10 and 7/10, respectively, in a perceptual evaluation with 1000 viewers. By combining physical-level illumination decoupling with semantic geometry a priori, this paper establishes a quantifiable feedback mechanism between objective metrics and human aesthetic evaluation, providing a new paradigm for aesthetic quality assessment of AI-generated content. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},
keywords = {3D faces, 3d facial model, 3D facial models, 3D modeling, adaptation, adult, Article, Audience perception evaluation, benchmarking, controlled study, Cross-modal, Face generation, Facial modeling, facies, Feature extraction, feedback, feedback system, female, Geometry, High-fidelity, human, illumination, Immersive media, Lighting, male, movie, Neural radiance field, Neural Radiance Fields, perception, Quality control, Rendering (computer graphics), Semantics, sensor, Three dimensional computer graphics, Virtual production, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this study proposes a cross-modal 3D face generation framework based on single-view semantic masks. It utilizes Swin Transformer for multi-level feature extraction and combines with NeRF for illumination decoupled rendering. We utilize physical rendering equations to explicitly separate surface reflectance from ambient lighting to achieve robust adaptation to complex lighting variations. In addition, to address geometric errors across illumination scenes, we construct geometric a priori constraint networks by mapping 2D facial features to 3D parameter space as regular terms with the help of semantic masks. On the CelebAMask-HQ dataset, this method achieves a leading score of SSIM = 0.892 (37.6% improvement from baseline) with FID = 40.6. The generated faces excel in symmetry and detail fidelity with realism and aesthetic scores of 8/10 and 7/10, respectively, in a perceptual evaluation with 1000 viewers. By combining physical-level illumination decoupling with semantic geometry a priori, this paper establishes a quantifiable feedback mechanism between objective metrics and human aesthetic evaluation, providing a new paradigm for aesthetic quality assessment of AI-generated content. © 2025 Elsevier B.V., All rights reserved.