AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

How to

Here you can find the complete list of our publications.
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.

Show all

2024

Chen, M.; Liu, M.; Wang, C.; Song, X.; Zhang, Z.; Xie, Y.; Wang, L.

Cross-Modal Graph Semantic Communication Assisted by Generative AI in the Metaverse for 6G Journal Article

In: Research, vol. 7, 2024, ISSN: 20965168 (ISSN).

Abstract | Links | BibTeX | Tags: 3-dimensional, 3Dimensional models, Cross-modal, Graph neural networks, Graph semantics, Metaverses, Multi-modal data, Point-clouds, Semantic communication, Semantic features, Semantics, Three dimensional computer graphics, Virtual scenario

@article{chen_cross-modal_2024,

title = {Cross-Modal Graph Semantic Communication Assisted by Generative AI in the Metaverse for 6G},

author = {M. Chen and M. Liu and C. Wang and X. Song and Z. Zhang and Y. Xie and L. Wang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192245049&doi=10.34133%2fresearch.0342&partnerID=40&md5=4a1c3e0a3ac877fcdf04937a96da32a1},

doi = {10.34133/research.0342},

issn = {20965168 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Research},

volume = {7},

abstract = {Recently, the development of the Metaverse has become a frontier spotlight, which is an important demonstration of the integration innovation of advanced technologies in the Internet. Moreover, artificial intelligence (AI) and 6G communications will be widely used in our daily lives. However, the effective interactions with the representations of multimodal data among users via 6G communications is the main challenge in the Metaverse. In this work, we introduce an intelligent cross-modal graph semantic communication approach based on generative AI and 3-dimensional (3D) point clouds to improve the diversity of multimodal representations in the Metaverse. Using a graph neural network, multimodal data can be recorded by key semantic features related to the real scenarios. Then, we compress the semantic features using a graph transformer encoder at the transmitter, which can extract the semantic representations through the cross-modal attention mechanisms. Next, we leverage a graph semantic validation mechanism to guarantee the exactness of the overall data at the receiver. Furthermore, we adopt generative AI to regenerate multimodal data in virtual scenarios. Simultaneously, a novel 3D generative reconstruction network is constructed from the 3D point clouds, which can transfer the data from images to 3D models, and we infer the multimodal data into the 3D models to increase realism in virtual scenarios. Finally, the experiment results demonstrate that cross-modal graph semantic communication, assisted by generative AI, has substantial potential for enhancing user interactions in the 6G communications and Metaverse. Copyright © 2024 Mingkai Chen et al.},

keywords = {3-dimensional, 3Dimensional models, Cross-modal, Graph neural networks, Graph semantics, Metaverses, Multi-modal data, Point-clouds, Semantic communication, Semantic features, Semantics, Three dimensional computer graphics, Virtual scenario},

pubstate = {published},

tppubtype = {article}

}