14 citations · 46 across the 13 of their papers we have counts for
14 papers
Gaussian-Mixture Latent Flow for Stochastic 3D Human Motion Prediction
Yue Ma, Frederick W. B. Li, Xiaohui Liang
Stochastic human motion prediction aims to forecast future motion distributions. Although recent studies have achieved strong performance in terms of accuracy and diversity, they o…
NeuroPath: Brain-Inspired Dual-Pathway Graph Convolutional Networks for Skeleton-Based Action Recognition
Kanglei Zhou, Ruizhi Cai, Hubert P. H. Shum +2
Skeleton-based action recognition aims to recognize human actions from sequences of human joint coordinates. Most existing Spatial-Temporal Graph Convolutional Networks (STGCNs) ha…
Two-Stage Multi-Modal Fusion with Adaptive Alignment for Action Quality Assessment
Kanglei Zhou, Ruizhi Cai, Xinning Wang +4
Action Quality Assessment (AQA) aims to evaluate how well a person performs a movement, which is essential in applications such as sports scoring, skill assessment, and healthcare.…
Quality-Preserving Imperceptible Adversarial Attack on Skeleton-based Human Action Recognition
Ziyi Chang, Kanglei Zhou, Xiaohui Liang +1
Adversarial attacks on skeletal human action recognition have received significant attention. However, existing methods typically introduce noise-like perturbations that degrade mo…
Fine-grained text-driven dual-human motion generation via dynamic hierarchical interaction
Mu Li, Yin Wang, Zhiying Leng +3
Human interaction is inherently dynamic and hierarchical, where the dynamic refers to the motion changes with distance, and the hierarchy is from individual to inter-individual and…
Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization
Kanglei Zhou, Qingyi Pan, Xingxing Zhang +4
Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation. A major challenge lies in the n…
Uncertainty-aware Probabilistic 3D Human Motion Forecasting via Invertible Networks
Yue Ma, Kanglei Zhou, Fuyang Yu +2
3D human motion forecasting aims to enable autonomous applications. Estimating uncertainty for each prediction (i.e., confidence based on probability density or quantile) is essent…
PHI: Bridging Domain Shift in Long-Term Action Quality Assessment via Progressive Hierarchical Instruction
Kanglei Zhou, Hubert P. H. Shum, Frederick W. B. Li +2
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 be…
FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment
Ruisheng Han, Kanglei Zhou, Amir Atapour-Abarghouei +2
Action quality assessment (AQA) is critical for evaluating athletic performance, informing training strategies, and ensuring safety in competitive sports. However, existing deep le…
Adaptive Score Alignment Learning for Continual Perceptual Quality Assessment of 360-Degree Videos in Virtual Reality
Kanglei Zhou, Zikai Hao, Liyuan Wang +1
Virtual Reality Video Quality Assessment (VR-VQA) aims to evaluate the perceptual quality of 360-degree videos, which is crucial for ensuring a distortion-free user experience. Tra…
A Comprehensive Survey of Action Quality Assessment: Method and Benchmark
Kanglei Zhou, Ruizhi Cai, Liyuan Wang +2
Action Quality Assessment (AQA) aims to automatically evaluate how well human actions are performed and has been widely applied in sports analysis, skill assessment, and healthcare…
CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine Instruction Alignment
Kanglei Zhou, Junlin Li, Ruizhi Cai +3
Action Quality Assessment (AQA) is pivotal for quantifying actions across domains like sports and medical care. Existing methods often rely on pre-trained backbones from large-scal…
MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment
Kanglei Zhou, Liyuan Wang, Xingxing Zhang +4
Action Quality Assessment (AQA) evaluates diverse skills but models struggle with non-stationary data. We propose Continual AQA (CAQA) to refine models using sparse new data. Featu…
Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model
Yin Wang, Zhiying Leng, Frederick W. B. Li +2
Text-driven human motion generation in computer vision is both significant and challenging. However, current methods are limited to producing either deterministic or imprecise moti…