Three-dimensional skeleton-based action recognition (3D SAR) has gained important
attention within the computer vision community, owing to the inherent advantages offered by …

Human skeleton, as a compact representation of human action, has received increasing
attention in recent years. Many skeleton-based action recognition methods adopt GCNs to …

This paper strives for self-supervised learning of a feature space suitable for skeleton-based
action recognition. Our proposal is built upon learning invariances to input skeleton …

In this paper, we propose an alignment network with iterative optimization for weakly
supervised continuous sign language recognition. Our framework consists of two modules: a …

Abstract We propose a Generative Adversarial Network (GAN) to forecast 3D human motion
given a sequence of past 3D skeleton poses. While recent GANs have shown promising …

Skeleton-based human action recognition is becoming increasingly important in a variety of
fields. Most existing works train a CNN or GCN based backbone to extract spatial-temporal …

Recently, skeleton-based action recognition becomes popular owing to the development of
cost-effective depth sensors and fast pose estimation algorithms. Traditional methods based …

Pose-based action recognition is predominantly tackled by approaches which treat the input
skeleton in a monolithic fashion, ie joints in the pose tree are processed as a whole …

The unsupervised representation learning for skeleton-based human action can be utilized
in a variety of pose analysis applications. However, previous unsupervised methods focus …

With the development of deep learning, skeleton-based action recognition has achieved
great progress in recent years. However, most of the current works focus on extracting more …