Human pose estimation aims at localizing human anatomical keypoints or body parts in the
input data (eg, images, videos, or signals). It forms a crucial component in enabling …

Temporal modeling is crucial for multi-frame human pose estimation. Most existing methods
directly employ optical flow or deformable convolution to predict full-spectrum motion fields …

Multi-person pose estimation is an attractive and challenging task. Existing methods are
mostly based on two-stage frameworks, which include top-down and bottom-up methods …

Good performance and high efficiency are both critical for estimating human pose in
practice. Recent state-of-the-art methods have greatly boosted the pose detection accuracy …

Semantic segmentation of 3D Light Detection and Ranging (LiDAR) indoor point clouds
using deep learning has been an active topic in recent years. However, most deep neural …

Aiming at capturing high-resolution spatial information and rich contextual information for
accurate positioning and inference of keypoints in the task of human pose estimation, a …

Current methods of multi-person pose estimation typically treat the localization and the
association of body joints separately. It is convenient but inefficient, leading to additional …

This paper discusses and studies the importance of Cross Dimensional Subspace Attention
for human pose estimation. The Cross Dimensional Subspace Attention module is to realize …

Accurately estimating the human pose is an essential task for many applications in robotics.
However, existing pose estimation methods suffer from poor performance when occlusion …

Human pose estimation aims to detect the keypoints of human body in the input images.
With the development of deep learning, human pose estimation has gradually matured and …