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Outputs (106)

Real-time and Controllable Reactive Motion Synthesis via Intention Guidance (2025)
Journal Article
Zhang, X., Chang, Z., Men, Q., & Shum, H. P. H. (online). Real-time and Controllable Reactive Motion Synthesis via Intention Guidance. Computer Graphics Forum, https://doi.org/10.1111/cgf.70222

We propose a real-time method for reactive motion synthesis based on the known trajectory of input character, predicting instant reactions using only historical, user-controlled motions. Our method handles the uncertainty of future movements by intro... Read More about Real-time and Controllable Reactive Motion Synthesis via Intention Guidance.

On the Design Fundamentals of Diffusion Models: A Survey (2025)
Journal Article
Chang, Z., Koulieris, G. A., Chang, H. J., & Shum, H. P. H. (2026). On the Design Fundamentals of Diffusion Models: A Survey. Pattern Recognition, 169, Article 111934. https://doi.org/10.1016/j.patcog.2025.111934

Diffusion models are learning pattern-learning systems to model and sample from data distributions with three functional components namely the forward process, the reverse process, and the sampling process. The components of diffusion models have gai... Read More about On the Design Fundamentals of Diffusion Models: A Survey.

PHI: Bridging Domain Shift in Long-Term Action Quality Assessment via Progressive Hierarchical Instruction (2025)
Journal Article
Zhou, K., Shum, H. P. H., Li, F. W. B., Zhang, X., & Liang, X. (2025). PHI: Bridging Domain Shift in Long-Term Action Quality Assessment via Progressive Hierarchical Instruction. IEEE Transactions on Image Processing, https://doi.org/10.1109/TIP.2025.3574938

Long-term Action Quality Assessment (AQA) aims to evaluate the quantitative performance of actions in long videos. However, existing methods face challenges due to domain shifts between the pre-trained large-scale action recognition backbones and the... Read More about PHI: Bridging Domain Shift in Long-Term Action Quality Assessment via Progressive Hierarchical Instruction.

Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos (2025)
Journal Article
Qiao, T., Li, R., Li, F. W. B., Kubotani, Y., Morishima, S., & Shum, H. P. H. (2025). Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos. Expert Systems with Applications, 290, Article 128344. https://doi.org/10.1016/j.eswa.2025.128344

Human-Object Interaction (HOI) recognition in videos requires understanding both visual patterns and geometric relationships as they evolve over time. Visual and geometric features offer complementary strengths. Visual features capture appearance con... Read More about Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos.

Large-Scale Multi-Character Interaction Synthesis (2025)
Presentation / Conference Contribution
Chang, Z., Wang, H., Koulieris, G. A., & Shum, H. P. (2025, August). Large-Scale Multi-Character Interaction Synthesis. Presented at ACM SIGGRAPH 2025, Vancouver, Canada

SEM-Net: Efficient Pixel Modelling for Image Inpainting with Spatially Enhanced SSM (2025)
Presentation / Conference Contribution
Chen, S., Zhang, H., Atapour-Abarghouei, A., & Shum, H. P. H. (2025, February). SEM-Net: Efficient Pixel Modelling for Image Inpainting with Spatially Enhanced SSM. Presented at 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Tucson, Arizona

Image inpainting aims to repair a partially damaged image based on the information from known regions of the images. Achieving semantically plausible inpainting results is particularly challenging because it requires the reconstructed regions to exhi... Read More about SEM-Net: Efficient Pixel Modelling for Image Inpainting with Spatially Enhanced SSM.

FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment (2025)
Presentation / Conference Contribution
Han, R., Zhou, K., Atapour-Abarghouei, A., Liang, X., & Shum, H. P. H. (2025, June). FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment. Presented at Proceedings of the 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025, Music City Center, Nashville TN

Action quality assessment (AQA) is critical for evaluating athletic performance, informing training strategies, and ensuring safety in competitive sports. However, existing deep learning approaches often operate as black boxes and are vulnerable to s... Read More about FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment.

BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction (2025)
Journal Article
Li, R., Katsigiannis, S., Kim, T.-K., & Shum, H. P. H. (online). BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction. IEEE Transactions on Neural Networks and Learning Systems, https://doi.org/10.1109/TNNLS.2025.3545268

Trajectory prediction allows better decision-making in applications of autonomous vehicles (AVs) or surveillance by predicting the short-term future movement of traffic agents. It is classified into pedestrian or heterogeneous trajectory prediction.... Read More about BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction.