Spatiotemporal human representation based on 3D visual perception data is a rapidly
growing research area. Representations can be broadly categorized into two groups …

The graph convolutional network (GCN) has recently achieved promising performance of 3D
human pose estimation (HPE) by modeling the relationship among body parts. However …

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for
regression. Current architectures of GCNs are limited to the small receptive field of …

We propose novel dynamic multiscale graph neural networks (DMGNN) to predict 3D
skeleton-based human motions. The core idea of DMGNN is to use a multiscale graph to …

Following the success of deep convolutional networks, state-of-the-art methods for 3d
human pose estimation have focused on deep end-to-end systems that predict 3d joint …

In this work, we address the problem of 3D human pose estimation from a sequence of 2D
human poses. Although the recent success of deep networks has led many state-of-the-art …

Despite the great progress in human motion prediction, it remains a challenging task due to
the complicated structural dynamics of human behaviors. In this paper, we address this …

With the rapid expansion of new available information presented to us online on a daily
basis, text classification becomes imperative in order to classify and maintain it. Word2vec …

Human motion modelling is a classical problem at the intersection of graphics and computer
vision, with applications spanning human-computer interaction, motion synthesis, and …

Human action recognition (HAR) in RGB-D videos has been widely investigated since the
release of affordable depth sensors. Currently, unimodal approaches (eg, skeleton-based …