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

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}

}