76 citations · 188 across the 14 of their papers we have counts for
9 papers · 1 filter
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction
Maosen Li, Siheng Chen, Zijing Zhang +3
Graph convolutional network based methods that model the body-joints' relations, have recently shown great promise in 3D skeleton-based human motion prediction. However, these meth…
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning
Chenxin Xu, Maosen Li, Zhenyang Ni +2
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works only con…
Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction
Maosen Li, Siheng Chen, Yangheng Zhao +3
We propose a multiscale spatio-temporal graph neural network (MST-GNN) to predict the future 3D skeleton-based human poses in an action-category-agnostic manner. The core of MST-GN…
Incremental Embedding Learning via Zero-Shot Translation
Kun Wei, Cheng Deng, Xu Yang +1
Modern deep learning methods have achieved great success in machine learning and computer vision fields by learning a set of pre-defined datasets. Howerver, these methods perform u…
Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation
Chenxin Xu, Siheng Chen, Maosen Li +1
We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learnin…
Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction
Maosen Li, Siheng Chen, Yangheng Zhao +3
We propose novel dynamic multiscale graph neural networks (DMGNN) to predict 3D skeleton-based human motions. The core idea of DMGNN is to use a multiscale graph to comprehensively…
Attribute Restoration Framework for Anomaly Detection
Chaoqin Huang, Fei Ye, Jinkun Cao +3
With the recent advances in deep neural networks, anomaly detection in multimedia has received much attention in the computer vision community. While reconstruction-based methods h…
Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction
Maosen Li, Siheng Chen, Xu Chen +3
3D skeleton-based action recognition and motion prediction are two essential problems of human activity understanding. In many previous works: 1) they studied two tasks separately,…
Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
Maosen Li, Siheng Chen, Xu Chen +3
Action recognition with skeleton data has recently attracted much attention in computer vision. Previous studies are mostly based on fixed skeleton graphs, only capturing local phy…