Human pose estimation aims to locate the human body parts and build human body
representation (eg, body skeleton) from input data such as images and videos. It has drawn …

Estimation of the human pose from a monocular camera has been an emerging research
topic in the computer vision community with many applications. Recently, benefiting from the …

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 …

In this paper, we are interested in the bottom-up paradigm of estimating human poses from
an image. We study the dense keypoint regression framework that is previously inferior to …

Human pose estimation deeply relies on visual clues and anatomical constraints between
parts to locate keypoints. Most existing CNN-based methods do well in visual …

While CNN-based models have made remarkable progress on human pose estimation,
what spatial dependencies they capture to localize keypoints remains unclear. In this work …

Human pose is typically represented by a coordinate vector of body joints or their heatmap
embeddings. While easy for data processing, unrealistic pose estimates are admitted due to …

High-resolution representations are essential for position-sensitive vision problems, such as
human pose estimation, semantic segmentation, and object detection. Existing state-of-the …

In this paper, we present a regression-based pose recognition method using cascade
Transformers. One way to categorize the existing approaches in this domain is to separate …

Vision transformers have achieved great successes in many computer vision tasks. Most
methods generate vision tokens by splitting an image into a regular and fixed grid and …