Recent skeleton-based action recognition approaches achieve great improvement by using
recurrent neural network (RNN) models. Currently, these approaches build an end-to-end …

Skeleton based action recognition distinguishes human actions using the trajectories of
skeleton joints, which provide a very good representation for describing actions …

Skeleton-based action recognition has made great progress recently, but many problems
still remain unsolved. For example, the representations of skeleton sequences captured by …

RNN-based approaches have achieved outstanding performance on action recognition with
skeleton inputs. Currently these methods limit their inputs to coordinates of joints and …

This paper addresses the problems of feature representation of skeleton joints and the
modeling of temporal dynamics to recognize human actions. Traditional methods generally …

Human actions can be represented by the trajectories of skeleton joints. Traditional methods
generally model the spatial structure and temporal dynamics of human skeleton with hand …

Skeleton-based action recognition aims to recognize human actions by exploring the
inherent characteristics from the given skeleton sequences and has attracted far more …

Skeleton-based human action recognition has attracted great interest thanks to the easy
accessibility of the human skeleton data. Recently, there is a trend of using very deep …

Current methods for skeleton-based human action recognition usually work with complete
skeletons. However, in real scenarios, it is inevitable to capture incomplete or noisy …

Skeleton based human action recognition has attracted more and more attentions recently
thanks to the accessibility of depth sensors and the development of pose estimation …