A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis (2023)
Journal Article
Zhou, K., Cai, R., Ma, Y., Tan, Q., Wang, X., Li, J., …Liang, X. (2023). A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis. IEEE Transactions on Visualization and Computer Graphics, 29(5), 2456-2466. https://doi.org/10.1109/tvcg.2023.3247092

As the most common idiopathic inflammatory myopathy in children, juvenile dermatomyositis (JDM) is characterized by skin rashes and muscle weakness. The childhood myositis assessment scale (CMAS) is commonly used to measure the degree of muscle invol... Read More about A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis.

Aesthetic Enhancement via Color Area and Location Awareness (2022)
Conference Proceeding
Yang, B., Wang, Q., Li, F. W., Liang, X., Wei, T., Zhu, C., …Noh, S. (2022). Aesthetic Enhancement via Color Area and Location Awareness. . https://doi.org/10.2312/pg.20221247

Choosing a suitable color palette can typically improve image aesthetic, where a naive way is choosing harmonious colors from some pre-defined color combinations in color wheels. However, color palettes only consider the usage of color types without... Read More about Aesthetic Enhancement via Color Area and Location Awareness.

Tackling Data Bias in Painting Classification with Style Transfer (2022)
Conference Proceeding
Vijendran, M., Li, F. W., & Shum, H. P. (in press). Tackling Data Bias in Painting Classification with Style Transfer. . https://doi.org/10.5220/0011776600003417

It is difficult to train classifiers on paintings collections due to model bias from domain gaps and data bias from the uneven distribution of artistic styles. Previous techniques like data distillation, traditional data augmentation and style transf... Read More about Tackling Data Bias in Painting Classification with Style Transfer.

STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos (2022)
Conference Proceeding
Almushyti, M., & Li, F. W. (2022). STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos. . https://doi.org/10.1109/icpr56361.2022.9956030

Recognizing human-object interactions is challenging due to their spatio-temporal changes. We propose the SpatioTemporal Interaction Transformer-based (STIT) network to reason such changes. Specifically, spatial transformers learn humans and objects... Read More about STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos.

Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos (2022)
Conference Proceeding
Qiao, T., Men, Q., Li, F. W., Kubotani, Y., Morishima, S., & Shum, H. P. (2022). Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos. . https://doi.org/10.1007/978-3-031-19772-7_28

Human-Object Interaction (HOI) recognition in videos is important for analysing human activity. Most existing work focusing on visual features usually suffer from occlusion in the real-world scenarios. Such a problem will be further complicated when... Read More about Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos.

Distillation of human–object interaction contexts for action recognition (2022)
Journal Article
Almushyti, M., & Li, F. W. (2022). Distillation of human–object interaction contexts for action recognition. Computer Animation and Virtual Worlds, 33(5), Article e2107. https://doi.org/10.1002/cav.2107

Modeling spatial-temporal relations is imperative for recognizing human actions, especially when a human is interacting with objects, while multiple objects appear around the human differently over time. Most existing action recognition models focus... Read More about Distillation of human–object interaction contexts for action recognition.

STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising (2021)
Conference Proceeding
Zhou, K., Cheng, Z., Shum, H. P., Li, F. W., & Liang, X. (2021). STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising. . https://doi.org/10.1109/ismar52148.2021.00018

Hand object interaction in mixed reality (MR) relies on the accurate tracking and estimation of human hands, which provide users with a sense of immersion. However, raw captured hand motion data always contains errors such as joints occlusion, disloc... Read More about STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising.

Facial reshaping operator for controllable face beautification (2020)
Journal Article
Hu, S., Shum, H. P., Liang, X., Li, F. W., & Aslam, N. (2021). Facial reshaping operator for controllable face beautification. Expert Systems with Applications, 167, Article 114067. https://doi.org/10.1016/j.eswa.2020.114067

Posting attractive facial photos is part of everyday life in the social media era. Motivated by the demand, we propose a lightweight method to automatically and efficiently beautify the shapes of both portrait and non-portrait faces in photos, while... Read More about Facial reshaping operator for controllable face beautification.

Target‐driven cloud evolution using position‐based fluids (2020)
Journal Article
Zhang, Z., Li, Y., Yang, B., Li, F. W., & Liang, X. (2020). Target‐driven cloud evolution using position‐based fluids. Computer Animation and Virtual Worlds, 31(6), https://doi.org/10.1002/cav.1937

To effectively control particle‐based cloud evolution without imposing strict position constraints, we propose a novel method integrating a control force field and a phase transition control into the position‐based fluids (PBF) framework. To produce... Read More about Target‐driven cloud evolution using position‐based fluids.

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., …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.