Skeleton-based action recognition has rapidly become one of the most popular and
essential research topics in computer vision. The task is to analyze the characteristics of …

There has been significant progress in skeleton-based action recognition. Human skeleton
can be naturally structured into graph, so graph convolution networks have become the most …

Graph convolutional networks (GCNs) can well-preserve the structure information of the
human body. They have achieved outstanding performance in skeleton-based action …

Skeleton-based action recognition aims to recognize human actions given human joint
coordinates with skeletal interconnections. By defining a graph with joints as vertices and …

We present PYSKL: an open-source toolbox for skeleton-based action recognition based on
PyTorch. The toolbox supports a wide variety of skeleton action recognition algorithms …

Recently, graph convolutional networks have achieved remarkable performance for skeleton-
based action recognition. In this work, we identify a problem posed by the GCNs for skeleton …

Graph convolutional networks (GCNs) are the most commonly used methods for skeleton-
based action recognition and have achieved remarkable performance. Generating …

Action recognition with skeleton data is attracting more attention in computer vision.
Recently, graph convolutional networks (GCNs), which model the human body skeletons as …

Skeleton-based action recognition is an important task that requires the adequate
understanding of movement characteristics of a human action from the given skeleton …

Body joints, directly obtained from a pose estimation model, have proven effective for action
recognition. Existing works focus on analyzing the dynamics of human joints. However …