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 …

Abstract Three-dimensional (3D) human pose estimation involves estimating the articulated
3D joint locations of a human body from an image or video. Due to its widespread …

Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity
and occlusion, which often lead to high uncertainty and indeterminacy. On the other hand …

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world.
Medical imaging such as X-ray and computed tomography (CT) plays an essential role in …

In this work, we demonstrate that 3D poses in video can be effectively estimated with a fully
convolutional model based on dilated temporal convolutions over 2D keypoints. We also …

Vision-based monocular human pose estimation, as one of the most fundamental and
challenging problems in computer vision, aims to obtain posture of the human body from …

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

We present a statistical, articulated 3D human shape modeling pipeline, within a fully
trainable, modular, deep learning framework. Given high-resolution complete 3D body …

Abstract In 2D-to-3D pose estimation, it is important to exploit the spatial constraints of 2D
joints, but it is not yet well modeled. To better model the relation of joints for 3D pose …

Existing 3D human pose estimators suffer poor generalization performance to new datasets,
largely due to the limited diversity of 2D-3D pose pairs in the training data. To address this …