The 2D-3D lifting task for Human Pose Estimation is a highly nonlinear mapping problem,
which requires mutual constraints among human joints. In this paper, we mainly discuss how …

Reconstructing 3D poses from 2D poses lacking depth information is particularly
challenging due to the complexity and diversity of human motion. The key is to effectively …

The study of deep end-to-end representation learning for 2D to 3D monocular human pose
estimation is a common yet challenging task in computer vision. However, current methods …

We present an approach for 3D human pose estimation from monocular images. The
approach consists of two steps: it first estimates a 2D pose from an image and then …

This study is about real-time 2D-3D human pose estimation without using the a priori
structure of the skeleton and with a low number of parameters for regression tasks. Current …

Despite the significant progress for monocular 3D human pose estimation, it still faces
challenges due to self-occlusions and depth ambiguities. To tackle those issues, we …

Three-dimensional human pose estimation (3D HPE) has broad application prospects in the
fields of trajectory prediction, posture tracking and action analysis. However, the frequent …

In this paper, we propose a novel 3D human pose estimation algorithm from a single image
based on neural networks. We adopted the structure of the relational networks in order to …

The combination of graph convolution network (GCN) and Transformer has shown
promising results in 3D human pose estimation (HPE) tasks when lifting the 2D to 3D poses …

Graph convolutional networks have significantly improved 3D human pose estimation by
representing the human skeleton as an undirected graph. However, this representation fails …