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 …

In this paper, we propose a novel graph convolutional network architecture, Graph Stacked
Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture …

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 …

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 …

Various deep learning techniques have been proposed to solve the single-view 2D-to-3D
pose estimation problem. While the average prediction accuracy has been improved …

End-to-end deep representation learning has achieved remarkable accuracy for monocular
3D human pose estimation, yet these models may fail for unseen poses with limited and …

Human poses that are rare or unseen in a training set are challenging for a network to
predict. Similar to the long-tailed distribution problem in visual recognition, the small number …

We present the first method to capture the 3D total motion of a target person from a
monocular view input. Given an image or a monocular video, our method reconstructs the …

Recovering human pose from RGB images and videos has drawn increasing attention in
recent years owing to minimum sensor requirements and applicability in diverse fields such …

A human pose is naturally represented as a graph where the joints are the nodes and the
bones are the edges. So it is natural to apply Graph Convolutional Network (GCN) to …