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Professor Hubert Shum's Outputs (98)

Cumuliform Cloud Formation Control using Parameter-Predicting Convolutional Neural Network (2020)
Journal Article
Zhang, Z., Ma, Y., Li, Y., Li, F. W., Shum, H. P., Yang, B., Guo, J., & Liang, X. (2020). Cumuliform Cloud Formation Control using Parameter-Predicting Convolutional Neural Network. Graphical Models, 111, Article 101083. https://doi.org/10.1016/j.gmod.2020.101083

Physically-based cloud simulation is an effective approach for synthesizing realistic cloud. However, generating clouds with desired shapes requires a time-consuming process for selecting the appropriate simulation parameters. This paper addresses su... Read More about Cumuliform Cloud Formation Control using Parameter-Predicting Convolutional Neural Network.

Sparse Metric-based Mesh Saliency (2020)
Journal Article
Hu, S., Liang, X., Shum, H. P., Li, F. W., & Aslam, N. (2020). Sparse Metric-based Mesh Saliency. Neurocomputing, 400, 11-23. https://doi.org/10.1016/j.neucom.2020.02.106

In this paper, we propose an accurate and robust approach to salient region detection for 3D polygonal surface meshes. The salient regions of a mesh are those that geometrically stand out from their contexts and therefore are semantically important f... Read More about Sparse Metric-based Mesh Saliency.

A Unified Deep Metric Representation for Mesh Saliency Detection and Non-rigid Shape Matching (2019)
Journal Article
Hu, S., Shum, H., Aslam, N., Li, F. W., & Liang, X. (2020). A Unified Deep Metric Representation for Mesh Saliency Detection and Non-rigid Shape Matching. IEEE Transactions on Multimedia, 22(9), 2278-2292. https://doi.org/10.1109/tmm.2019.2952983

In this paper, we propose a deep metric for unifying the representation of mesh saliency detection and non-rigid shape matching. While saliency detection and shape matching are two closely related and fundamental tasks in shape analysis, previous met... Read More about A Unified Deep Metric Representation for Mesh Saliency Detection and Non-rigid Shape Matching.

Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling (2019)
Journal Article
Wang, H., Ho, E. S., Shum, H. P., & Zhu, Z. (2021). Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling. IEEE Transactions on Visualization and Computer Graphics, 27(1), 216 - 227. https://doi.org/10.1109/tvcg.2019.2936810

Data-driven modeling of human motions is ubiquitous in computer graphics and vision applications. Such problems can be approached by deep learning on a large amount data. However, existing methods can be sub-optimal for two reasons. First, skeletal i... Read More about Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling.

Interaction-Based Human Activity Comparison (2019)
Journal Article
Shen, Y., Yang, L., Ho, E. S., & Shum, H. P. (2020). Interaction-Based Human Activity Comparison. IEEE Transactions on Visualization and Computer Graphics, 26(8), 2620-2633. https://doi.org/10.1109/tvcg.2019.2893247

Traditional methods for motion comparison consider features from individual characters. However, the semantic meaning of many human activities is usually defined by the interaction between them, such as a high-five interaction of two characters. Ther... Read More about Interaction-Based Human Activity Comparison.

Automatic Musculoskeletal and Neurological Disorder Diagnosis With Relative Joint Displacement From Human Gait (2018)
Journal Article
Rueangsirarak, W., Zhang, J., Aslam, N., Ho, E. S., & Shum, H. P. (2018). Automatic Musculoskeletal and Neurological Disorder Diagnosis With Relative Joint Displacement From Human Gait. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(12), 2387-2396. https://doi.org/10.1109/tnsre.2018.2880871

Musculoskeletal and neurological disorders are common devastating companions of ageing, leading to a reduction in quality of life and increased mortality. Gait analysis is a popular method for diagnosing these disorders. However, manually analyzing t... Read More about Automatic Musculoskeletal and Neurological Disorder Diagnosis With Relative Joint Displacement From Human Gait.

Action Recognition From Arbitrary Views Using Transferable Dictionary Learning (2018)
Journal Article
Zhang, J., Shum, H. P., Han, J., & Shao, L. (2018). Action Recognition From Arbitrary Views Using Transferable Dictionary Learning. IEEE Transactions on Image Processing, 27(10), 4709-4723. https://doi.org/10.1109/tip.2018.2836323

Human action recognition is crucial to many practical applications, ranging from human-computer interaction to video surveillance. Most approaches either recognize the human action from a fixed view or require the knowledge of view angle, which is us... Read More about Action Recognition From Arbitrary Views Using Transferable Dictionary Learning.

Manifold Regularized Experimental Design for Active Learning (2016)
Journal Article
Zhang, L., Shum, H. P., & Shao, L. (2017). Manifold Regularized Experimental Design for Active Learning. IEEE Transactions on Image Processing, 26(2), 969-981. https://doi.org/10.1109/tip.2016.2635440

Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many p... Read More about Manifold Regularized Experimental Design for Active Learning.

Validation of an ergonomic assessment method using Kinect data in real workplace conditions (2016)
Journal Article
Plantard, P., Shum, H. P., Le Pierres, A.-S., & Multon, F. (2017). Validation of an ergonomic assessment method using Kinect data in real workplace conditions. Applied Ergonomics: Human Factors in Technology and Society, 65, 562-569. https://doi.org/10.1016/j.apergo.2016.10.015

Evaluating potential musculoskeletal disorders risks in real workstations is challenging as the environment is cluttered, which makes it difficult to accurately assess workers' postures. Being marker-free and calibration-free, Microsoft Kinect is a p... Read More about Validation of an ergonomic assessment method using Kinect data in real workplace conditions.

Discriminative Semantic Subspace Analysis for Relevance Feedback (2016)
Journal Article
Zhang, L., Shum, H., & Shao, L. (2016). Discriminative Semantic Subspace Analysis for Relevance Feedback. IEEE Transactions on Image Processing, 25(3), 1275-1287. https://doi.org/10.1109/tip.2016.2516947

Content-based image retrieval (CBIR) has attracted much attention during the past decades for its potential practical applications to image database management. A variety of relevance feedback (RF) schemes have been designed to bridge the gap between... Read More about Discriminative Semantic Subspace Analysis for Relevance Feedback.

Kinect Posture Reconstruction Based on a Local Mixture of Gaussian Process Models (2015)
Journal Article
Liu, Z., Zhou, L., Leung, H., & Shum, H. P. (2016). Kinect Posture Reconstruction Based on a Local Mixture of Gaussian Process Models. IEEE Transactions on Visualization and Computer Graphics, 22(11), 2437-2450. https://doi.org/10.1109/tvcg.2015.2510000

Depth sensor based 3D human motion estimation hardware such as Kinect has made interactive applications more popular recently. However, it is still challenging to accurately recognize postures from a single depth camera due to the inherently noisy da... Read More about Kinect Posture Reconstruction Based on a Local Mixture of Gaussian Process Models.

Real-Time Posture Reconstruction for Microsoft Kinect (2013)
Journal Article
Shum, H. P., Ho, E. S., Jiang, Y., & Takagi, S. (2013). Real-Time Posture Reconstruction for Microsoft Kinect. IEEE Transactions on Cybernetics, 43(5), 1357-1369. https://doi.org/10.1109/tcyb.2013.2275945

The recent advancement of motion recognition using Microsoft Kinect stimulates many new ideas in motion capture and virtual reality applications. Utilizing a pattern recognition algorithm, Kinect can determine the positions of different body parts fr... Read More about Real-Time Posture Reconstruction for Microsoft Kinect.

Interactive Formation Control in Complex Environments (2013)
Journal Article
Henry, J., Shum, H. P., & Komura, T. (2014). Interactive Formation Control in Complex Environments. IEEE Transactions on Visualization and Computer Graphics, 20(2), 211-222. https://doi.org/10.1109/tvcg.2013.116

The degrees of freedom of a crowd is much higher than that provided by a standard user input device. Typically, crowd-control systems require multiple passes to design crowd movements by specifying waypoints, and then defining character trajectories... Read More about Interactive Formation Control in Complex Environments.

Simulating Multiple Character Interactions with Collaborative and Adversarial Goals (2010)
Journal Article
Shum, H. P., Komura, T., & Yamazaki, S. (2012). Simulating Multiple Character Interactions with Collaborative and Adversarial Goals. IEEE Transactions on Visualization and Computer Graphics, 18(5), 741-752. https://doi.org/10.1109/tvcg.2010.257

This paper proposes a new methodology for synthesizing animations of multiple characters, allowing them to intelligently compete with one another in dense environments, while still satisfying requirements set by an animator. To achieve these two conf... Read More about Simulating Multiple Character Interactions with Collaborative and Adversarial Goals.

Interaction patches for multi-character animation (2008)
Journal Article
Shum, H. P., Komura, T., Shiraishi, M., & Yamazaki, S. (2008). Interaction patches for multi-character animation. ACM Transactions on Graphics, 27(5), Article 114. https://doi.org/10.1145/1409060.1409067

We propose a data-driven approach to automatically generate a scene where tens to hundreds of characters densely interact with each other. During off-line processing, the close interactions between characters are precomputed by expanding a game tree,... Read More about Interaction patches for multi-character animation.