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 …

In action recognition, although the combination of spatio-temporal videos and skeleton
features can improve the recognition performance, a separate model and balancing feature …

We present a unified perspective on tackling various human-centric video tasks by learning
human motion representations from large-scale and heterogeneous data resources …

Despite the great progress in 3D human pose estimation from videos, it is still an open
problem to take full advantage of a redundant 2D pose sequence to learn representative …

Human pose estimation (HPE) has developed over the past decade into a vibrant field for
research with a variety of real-world applications like 3D reconstruction, virtual testing and re …

Modeling spatiotemporal global dependencies and dynamics of body joints are crucial to
recognizing actions from 3D skeleton sequences. We propose a Relation-mining Self …

We present SkeleTR, a new framework for skeleton-based action recognition. In contrast to
prior work, which focuses mainly on controlled environments, we target in-the-wild scenarios …

Graph convolutional networks (GCNs) are widely adopted in skeleton-based action
recognition due to their powerful ability to model data topology. We argue that the …

Despite great progress achieved by transformer in various vision tasks, it is still
underexplored for skeleton-based action recognition with only a few attempts. Besides …

Graph convolution networks (GCN) have been widely used in skeleton-based action
recognition. We note that existing GCN-based approaches primarily rely on prescribed …