Outputs (5)

CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy (2022)
Journal Article
Zhang, H., Ho, E. S., & Shum, H. P. (2022). CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy. Software impacts, 14, Article 100419. https://doi.org/10.1016/j.simpa.2022.100419

Early prediction is clinically considered one of the essential parts of cerebral palsy (CP) treatment. We propose to implement a low-cost and interpretable classification system for supporting CP prediction based on General Movement Assessment (GMA).... Read More about CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy.

Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models
Presentation / Conference Contribution
Chang, Z., Findlay, E. J., Zhang, H., & Shum, H. P. (2023, February). Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models. Presented at GRAPP 2023: 2023 International Conference on Computer Graphics Theory and Applications, Lisbon, Portugal

Generating realistic motions for digital humans is a core but challenging part of computer animations and games, as human motions are both diverse in content and rich in styles. While the latest deep learning approaches have made significant advancem... Read More about Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models.

Denoising Diffusion Probabilistic Models for Styled Walking Synthesis
Presentation / Conference Contribution
Findlay, E., Zhang, H., Chang, Z., & Shum, H. P. (2022, November). Denoising Diffusion Probabilistic Models for Styled Walking Synthesis. Presented at MIG 2022: The 15th Annual ACM SIGGRAPH Conference on Motion, Interaction and Games, Guanajuato, Mexico

Generating realistic motions for digital humans is time-consuming for many graphics applications. Data-driven motion synthesis approaches have seen solid progress in recent years through deep generative models. These results offer high-quality motion... Read More about Denoising Diffusion Probabilistic Models for Styled Walking Synthesis.

Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks
Presentation / Conference Contribution
Zhang, H., Shum, H. P., & Ho, E. S. (2022, July). Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks. Presented at 2022 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Glasgow

Early diagnosis and intervention are clinically considered the paramount part of treating cerebral palsy (CP), so it is essential to design an efficient and interpretable automatic prediction system for CP. We highlight a significant difference betwe... Read More about Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks.

Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video
Presentation / Conference Contribution
Zhang, X., Zhang, H., & Shum, H. P. (2022, September). Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video. Presented at MICCAI '22: The 25th International Conference on Medical Image Computing and Computer Assisted Intervention, Singapore

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that results in a variety of motor dysfunction symptoms, including tremors, bradykinesia, rigidity and postural instability. The diagnosis of PD mainly relies on clinical experience... Read More about Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video.