Point clouds are increasingly being used to improve productivity, quality, and safety
throughout the life cycle of construction and infrastructure projects. While applicable for …

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

Deep neural networks based purely on attention have been successful across several
domains, relying on minimal architectural priors from the designer. In Human Action …

Abstract Skeleton-based Human Activity Recognition has achieved great interest in recent
years as skeleton data has demonstrated being robust to illumination changes, body scales …

Skeleton sequence representation learning has shown great advantages for action
recognition due to its promising ability to model human joints and topology. However, the …

Skeleton-based human action recognition has achieved a great interest in recent years, as
skeleton data has been demonstrated to be robust to illumination changes, body scales …

Skeleton-based human action recognition has caused wide concern, as skeleton data can
robustly adapt to dynamic circumstances such as camera view changes and background …

Human motion prediction aims to generate future motions based on the observed human
motions. Witnessing the success of Recurrent Neural Networks (RNN) in modeling …

We propose a new transformer model for the task of unsupervised learning of skeleton
motion sequences. The existing transformer model utilized for unsupervised skeleton-based …

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 …