Dr. Shengyong Ding

Associate professor, doctoral supervisor/master supervisor 

Email：syding@cityu.edu.mo

Tel: (853)85902428
Office address:  Room S401, Stanley Ho Building, City University of Macau (Taipa)

Educational qualifications 

2015: PhD in Computer Applications, Sun Yat-sen University, China

2008: Master of Applied Mathematics, Sun Yat-sen University, China

2001: Bachelor of Information Management, University of Science and Technology of China, China

Current Position

Associate Professor of Data Science, City University of Macau

Previously Taught Subjects

BCS011           Introduction to Operating System

BIT036            Visualization and Computer Graphics

Research Interests

Fundamental Research: Integrating large model technology with traditional computer vision (CV) techniques to solve key challenges in machine vision, such as pose estimation, 3D reconstruction and generation, and spatial navigation, with a focus on model geometric quality.

• 3D Avatar: Identity (ID)-preserving 3D avatar reconstruction and generation. Research on how to obtain high-quality 3D realistic or stylized models from a small number of RGB images by integrating traditional CV techniques, large model technology, and reinforcement learning to solve key challenges.
• Video3D: Integrating video generation with 3D vision. Research on how to introduce 3D constraints into video generation and organically merge 2D video generation with 3D model reconstruction/generation processes, with a focus on maintaining 3D consistency of human figures.
• MAgent: Multi-agent systems for multimodal generation aim to explore how to use agent technologies to automatically orchestrate language, speech, and video models for creating multimedia content.

Applied Research: Applying advanced AI models/tools to solve specific application problems in fields such as education and healthcare.

• Multiagent Story Generation: Apply large generative models such as language models, voice models and video models to generate video stories based on users’ simple inputs.
• Educational and Medical Data Analysis: Utilize facial, body posture, and multimodal perception technologies to analyze students' attention in real-time, providing key core capabilities for smart education; explore the applications of artificial intelligence in fields such as traditional Chinese medicine heritage and vestibular therapy.
• Smart Transportation: Explore the applications of traditional vision technologies such as stereo vision, LiDAR, and multimodal large models in smart transportation inspection.

Research & Publications

Refereed Journal Articles

Liu, Y., Liu, X., Ding, S., & Yang, Z. (2025). Robustly solving PnL problem using Clifford tori. Pattern Recognit., 166, 111659.

Xiao, Zelin & Lin, Hongxin & Li, Renjie & Geng, Lishuai & Chao, Hongyang & Ding, Shengyong. (2020). Endowing Deep 3d Models With Rotation Invariance Based On Principal Component Analysis. 1-6. 10.1109/ICME46284.2020.9102947.

Lin, Hongxin & Xiao, Zelin & Tan, Yang & Chao, Hongyang & Ding, Shengyong. (2019). Justlookup: One Millisecond Deep Feature Extraction for Point Clouds By Lookup Tables. 326-331. 10.1109/ICME.2019.00064.

Tan, Yang & Lin, Hongxin & Xiao, Zelin & Ding, Shengyong & Chao, Hongyang. (2019). Face Recognition from Sequential Sparse 3D Data via Deep Registration. 1-8. 10.1109/ICB45273.2019.8987284.

Wang, Guangrun & Lin, Liang & Ding, Shengyong & Li, Ya & Wang, Qing. (2016). DARI: Distance Metric and Representation Integration for Person Verification. Proceedings of the AAAI Conference on Artificial Intelligence. 30. 10.1609/aaai.v30i1.10462.

Ding, Shengyong & Lin, Liang & Wang, Guangrun & Chao, Hongyang. J(2015). Deep Feature Learning with Relative Distance Comparison for Person Re-identification. Pattern Recognition. 48. 10.1016/j.patcog.2015.04.005.

Academic Awards

2018 Pattern Recognition Best Paper