We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We
start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution …

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

Depth ambiguity is one of the main challenges of three-dimensional (3D) human pose
estimation (HPE). The recent strategies of disambiguating have brought significant progress …

High-resolution networks have made significant progress in dense prediction tasks such as
human pose estimation and semantic segmentation. To better explore this high-resolution …

Graph neural networks (GNNs) have been widely used in the 3D human pose estimation
task, since the pose representation of a human body can be naturally modeled by the graph …

Vision-based 3D human pose estimation approaches are typically evaluated on datasets
that are limited in diversity regarding many factors, eg, subjects, poses, cameras, and …

In the everyday IoT ecosystem, many devices and systems are interconnected in an
intelligent living environment to create a comfortable and efficient living space. In this …

Various structural relations/dependencies exist among human body joints, which makes it
possible to estimate 3D poses from 2D sources. The current research on 3D human pose …

Abstract 3D Human Pose Estimation (3D-HPE) is a highly active and evolving research area
in computer vision with numerous applications such as extended reality, action recognition …

Graph convolutional networks (GCNs) have proven to be an effective approach for 3D
human pose estimation. By naturally modeling the skeleton structure of the human body as a …