Dr Hyung Jin Chang PhD, FHEA

School of Computer Science
Associate Professor in Computer Science
Head of External Partnerships of Computer Science

Contact details

Email: h.j.chang@bham.ac.uk

Address: The School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Hyung Jin Chang is an Associate Professor in Computer Science at the University of Birmingham. His research focuses on human-centred AI and computer vision for intelligent and interactive robotic systems.

Qualifications

PhD in Electrical Engineering and Computer Science, Seoul National University, South Korea, 2013
BSc in Electrical and Computer Engineering, Seoul National University, South Korea, 2006
Fellow of the Higher Education Academy (FHEA)

Biography

Dr Hyung Jin Chang is an Associate Professor in the School of Computer Science at the University of Birmingham and is affiliated with the Institute of Data and Artificial Intelligence (IDAI). He was also a University Turing Fellow of the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence.

He received his BSc in Electrical and Computer Engineering and PhD in Electrical Engineering and Computer Science from Seoul National University in 2006 and 2013, respectively. Before joining the University of Birmingham, he worked as a Post-Doctoral Research Associate at Imperial College London, where he participated in several European research projects focusing on computer vision, machine learning, and human-robot interaction.

Dr Chang’s research lies at the intersection of artificial intelligence, computer vision, robotics, and human-robot interaction, with a particular emphasis on human-centred AI. His work aims to build intelligent robotic systems capable of understanding human perception, action, and intention through visual and multimodal learning.

He is especially interested in human-centred visual learning, including the estimation of human hand pose, body pose, and gaze, and the modelling of human actions and internal states. His broader goal is to advance robot vision and learning for intelligent and natural human–robot interaction.

His research has been supported by national and international funding agencies and industrial partners, and his work has appeared in leading venues such as CVPR, ICCV, ECCV, ICRA, IROS, NeurIPS, and AAAI. He also serves regularly as Area Chair and reviewer for top-tier AI and robotics conferences. He is currently an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

For more information, please visit his personal webpage.

Postgraduate supervision

Dr Chang supervises doctoral research in the following areas:

Human-centred AI and visual learning
Computer vision for human understanding
Robot perception and dexterous manipulation
Human–robot interaction

He welcomes PhD applicants interested in the intersection of computer vision, robotics, and human-centred intelligence.

Research

Dr Chang’s research combines artificial intelligence, computer vision, and robotics to enable intelligent and interactive robotic systems. His work explores both fundamental visual learning problems and applied robotic perception.

Research themes

Human-Centred Visual Learning – Modelling human perception and behaviour through hand, body, and gaze estimation
Robot Vision and Dexterity – Enabling robots to perceive and manipulate objects intelligently in dynamic environments
Multimodal Learning for Interaction – Integrating vision, touch, and force information for adaptive and safe robot control

He collaborates closely with industrial and academic partners and contributes to UK and EU-funded research programmes on intelligent robot perception.

Publications

Recent publications

Article

Chun, S, Kim, B, Chang, HJ & Chang, JY 2026, 'Bidirectional regression for monocular 6DoF head pose estimation and reference system alignment', Pattern Recognition, vol. 179, no. Part A, 113585. https://doi.org/10.1016/j.patcog.2026.113585

Chang, Z, Koulieris, GA, Chang, HJ & Shum, HPH 2026, 'On the Design Fundamentals of Diffusion Models: A Survey', Pattern Recognition, vol. 169, 111934. https://doi.org/10.1016/j.patcog.2025.111934

Kim, B, Kim, J, Chang, HJ & Oh, T-H 2025, 'A unified framework for unsupervised action learning via global-to-local motion transformer', Pattern Recognition, vol. 159, 111118. https://doi.org/10.1016/j.patcog.2024.111118

Zhou, Y, Gou, C, Guo, Z, Cheng, Y & Chang, HJ 2025, 'Behavior-Aware Knowledge-Embedded Model for Driver Attention Prediction', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 10, pp. 10199-10212. https://doi.org/10.1109/TCSVT.2025.3565410

Na, J, Jung, H, Chang, HJ & Hwang, W 2025, 'Bridging domain spaces for unsupervised domain adaptation', Pattern Recognition, vol. 164, 111537. https://doi.org/10.1016/j.patcog.2025.111537

Conference contribution

Zhong, H, Tang, F, Chang, HJ & Gao, Y 2026, AMDANet: Attention-Driven Multi-Perspective Discrepancy Alignment for RGB-Infrared Image Fusion and Segmentation. in 2025 IEEE/CVF International Conference on Computer Vision (ICCV). International Conference on Computer Vision (ICCV), IEEE, pp. 10645-10655, International Conference on Computer Vision 2025, Honolulu, Hawaii, United States, 19/10/25. https://doi.org/10.1109/ICCV51701.2025.00991

Chen, Z, Zhang, Z, Cheng, Y, Leonardis, A & Chang, HJ 2026, Force-Aware 3D Contact Modeling for Stable Grasp Generation. in S Koenig, C Jenkins & ME Taylor (eds), Proceedings of the 40th AAAI Conference on Artificial Intelligence: AAAI-26 Technical Tracks 5. Proceedings of the AAAI Conference on Artificial Intelligence, no. 5, vol. 40, Association for the Advancement of Artificial Intelligence, pp. 3219-3227, 40th AAAI Conference on Artificial Intelligence, Singapore, Singapore, 20/01/26. https://doi.org/10.1609/aaai.v40i5.37316

Zhong, H, Han, W, Wang, Z, Feng, R, Tang, F, Lee, T-Y, Fan, Z, Wu, R, Wang, Y, Dong, H, Chen, H, Chang, HJ & Gao, Y 2026, GraspALL: Adaptive Structural Compensation from Illumination Variation for Robotic Garment Grasping in Any Low-Light Conditions. in 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Denver, Colorado, United States, 3/06/26.

Feng, R, Chang, HJ, Tse, THE, Kim, B, Chang, Y & Gao, Y 2026, High-Resolution Spatiotemporal Modeling with Global-Local State Space Models for Video-Based Human Pose Estimation. in 2025 IEEE/CVF International Conference on Computer Vision (ICCV). International Conference on Computer Vision (ICCV), IEEE, pp. 8929-8938, International Conference on Computer Vision 2025, Honolulu, Hawaii, United States, 19/10/25. https://doi.org/10.1109/ICCV51701.2025.00835

Jin, H, Chang, HJ & Kim, E 2026, Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models. in 2025 IEEE/CVF International Conference on Computer Vision (ICCV). International Conference on Computer Vision (ICCV), IEEE, pp. 3466-3476, International Conference on Computer Vision 2025, Honolulu, Hawaii, United States, 19/10/25. https://doi.org/10.1109/ICCV51701.2025.00331

Wang, H, Zhang, Z, Cheng, Y & Chang, HJ 2026, RTGaze: Real-Time 3D-Aware Gaze Redirection from a Single Image. in S Koenig, C Jenkins & ME Taylor (eds), Proceedings of the 40th AAAI Conference on Artificial Intelligence: AAAI-26 Technical Tracks 12. Proceedings of the AAAI Conference on Artificial Intelligence, no. 12, vol. 40, Association for the Advancement of Artificial Intelligence, pp. 9784-9792, 40th AAAI Conference on Artificial Intelligence, Singapore, Singapore, 20/01/26. https://doi.org/10.1609/aaai.v40i12.37942

Dasgupta, E, Kim, B, Yeo, SH & Chang, HJ 2026, SimForce: Force and Surface Electromyography from Full Body Video with Graph Neural Nets. in 2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE Workshop on Applications of Computer Vision (WACV), IEEE, 2026 IEEE/CVF Winter Conference on Applications of Computer Vision, Tucson, Arizona, United States, 6/03/26. https://doi.org/10.1109/WACV61042.2026.00324

Jeong, U, Tiruneh, YY, Chang, HJ, Baek, S & Kim, KI 2026, THOM: Generating Physically Plausible Hand-Object Meshes From Text. in 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Denver, Colorado, United States, 3/06/26.

Hu, H, Lin, D, Huang, Q, Hou, Y, Chang, HJ & Jiao, J 2025, Audio-Visual Separation with Hierarchical Fusion and Representation Alignment. in 36th British Machine Vision Conference 2025, BMVC 2025, Sheffield, UK, November 24-27, 2025., 493, BMVA , The 36th British Machine Vision Conference 2025, Sheffield, United Kingdom, 24/11/25. <https://bmvc2025.bmva.org/proceedings/493/>

Preprint

Tse, THE, Feng, R, Zheng, L, Park, J, Gao, Y, Kim, J, Leonardis, A & Chang, HJ 2025 'Collaborative Learning for 3D Hand-Object Reconstruction and Compositional Action Recognition from Egocentric RGB Videos Using Superquadrics' arXiv. https://doi.org/10.48550/arXiv.2501.07100

View all publications in research portal