1.8k citations · 1.8k across the 4 of their papers we have counts for
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Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning
Siyuan Yang, Jun Liu, Shijian Lu +2
Skeleton-based human action recognition has attracted increasing attention in recent years. However, most of the existing works focus on supervised learning which requiring a large…
TrajectoryNet: a new spatio-temporal feature learning network for human motion prediction
Xiaoli Liu, Jianqin Yin, Jin Liu +3
Human motion prediction is an increasingly interesting topic in computer vision and robotics. In this paper, we propose a new 2D CNN based network, TrajectoryNet, to predict future…
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Jun Liu, Amir Shahroudy, Mauricio Perez +3
Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-…
Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection
Hengduo Li, Jun Liu, Guyue Zhang +2
With the development of depth cameras such as Kinect and Intel Realsense, RGB-D based human detection receives continuous research attention due to its usage in a variety of applic…
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates
Jun Liu, Amir Shahroudy, Dong Xu +2
Skeleton-based human action recognition has attracted a lot of research attention during the past few years. Recent works attempted to utilize recurrent neural networks to model th…
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Amir Shahroudy, Jun Liu, Tian-Tsong Ng +1
Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes. Cu…