AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
How to
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
Tong, Y.; Qiu, Y.; Li, R.; Qiu, S.; Heng, P. -A.
MS2Mesh-XR: Multi-Modal Sketch-to-Mesh Generation in XR Environments Proceedings Article
In: Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR, pp. 272–276, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833152157-8 (ISBN).
Abstract | Links | BibTeX | Tags: 3D meshes, 3D object, ControlNet, Hand-drawn sketches, Hands movement, High quality, Image-based, immersive visualization, Mesh generation, Multi-modal, Pipeline codes, Realistic images, Three dimensional computer graphics, Virtual environments, Virtual Reality
@inproceedings{tong_ms2mesh-xr_2025,
title = {MS2Mesh-XR: Multi-Modal Sketch-to-Mesh Generation in XR Environments},
author = {Y. Tong and Y. Qiu and R. Li and S. Qiu and P. -A. Heng},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000423684&doi=10.1109%2fAIxVR63409.2025.00052&partnerID=40&md5=caeace6850dcbdf8c1fa0441b98fa8d9},
doi = {10.1109/AIxVR63409.2025.00052},
isbn = {979-833152157-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},
pages = {272–276},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {We present MS2Mesh-XR, a novel multimodal sketch-to-mesh generation pipeline that enables users to create realistic 3D objects in extended reality (XR) environments using hand-drawn sketches assisted by voice inputs. In specific, users can intuitively sketch objects using natural hand movements in mid-air within a virtual environment. By integrating voice inputs, we devise ControlNet to infer realistic images based on the drawn sketches and interpreted text prompts. Users can then review and select their preferred image, which is subsequently reconstructed into a detailed 3D mesh using the Convolutional Reconstruction Model. In particular, our proposed pipeline can generate a high-quality 3D mesh in less than 20 seconds, allowing for immersive visualization and manipulation in runtime XR scenes. We demonstrate the practicability of our pipeline through two use cases in XR settings. By leveraging natural user inputs and cutting-edge generative AI capabilities, our approach can significantly facilitate XR-based creative production and enhance user experiences. Our code and demo will be available at: https://yueqiu0911.github.io/MS2Mesh-XR/. © 2025 IEEE.},
keywords = {3D meshes, 3D object, ControlNet, Hand-drawn sketches, Hands movement, High quality, Image-based, immersive visualization, Mesh generation, Multi-modal, Pipeline codes, Realistic images, Three dimensional computer graphics, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
2023
Wang, J.; Chen, S.; Liu, Y.; Lau, R.
Intelligent Metaverse Scene Content Construction Journal Article
In: IEEE Access, vol. 11, pp. 76222–76241, 2023, ISSN: 21693536 (ISSN).
Abstract | Links | BibTeX | Tags: Bridges, Content generation, Contents constructions, Current situation, Deep learning, immersive visualization, Intelligent Agents, Metaverse, Metaverses, Solid modelling, Three dimensional computer graphics, Three dimensional displays, Three-dimensional display, Virtual Reality, Visual content, Visualization
@article{wang_intelligent_2023,
title = {Intelligent Metaverse Scene Content Construction},
author = {J. Wang and S. Chen and Y. Liu and R. Lau},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165350593&doi=10.1109%2fACCESS.2023.3297873&partnerID=40&md5=6004d639bc6313f19a1276588c6d092c},
doi = {10.1109/ACCESS.2023.3297873},
issn = {21693536 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {IEEE Access},
volume = {11},
pages = {76222–76241},
abstract = {The integration of artificial intelligence (AI) and virtual reality (VR) has revolutionized research across various scientific fields, with AI-driven VR simulations finding applications in education, healthcare, and entertainment. However, existing literature lacks a comprehensive investigation that systematically summarizes the fundamental characteristics and development trajectory of AI-generated visual content in the metaverse. This survey focuses on intelligent metaverse scene content construction, aiming to address this gap by exploring the application of AI in content generation. It investigates scene content generation, simulation biology, personalized content, and intelligent agents. Analyzing the current state and identifying common features, this survey provides a detailed description of methods for constructing intelligent metaverse scenes. The primary contribution is a comprehensive analysis of the current landscape of intelligent visual content production in the metaverse, highlighting emerging trends. The discussion on methods for constructing intelligent scene content in the metaverse suggests that in the era of intelligence, it has the potential to become the dominant approach for content creation in metaverse scenes. © 2013 IEEE.},
keywords = {Bridges, Content generation, Contents constructions, Current situation, Deep learning, immersive visualization, Intelligent Agents, Metaverse, Metaverses, Solid modelling, Three dimensional computer graphics, Three dimensional displays, Three-dimensional display, Virtual Reality, Visual content, Visualization},
pubstate = {published},
tppubtype = {article}
}