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 …

Biological systems understand the world by simultaneously processing high-dimensional
inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The …

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 are interested in the human pose estimation problem with a focus on
learning reliable high-resolution representations. Most existing methods recover high …

State-of-the-art human pose estimation methods are based on heat map representation. In
spite of the good performance, the representation has a few issues in nature, such as non …

We present the first real-time method to capture the full global 3D skeletal pose of a human
in a stable, temporally consistent manner using a single RGB camera. Our method combines …

We propose a CNN-based approach for 3D human body pose estimation from single RGB
images that addresses the issue of limited generalizability of models trained solely on the …

We introduce associative embedding, a novel method for supervising convolutional neural
networks for the task of detection and grouping. A number of computer vision problems can …

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 …

In this paper, we propose to incorporate convolutional neural networks with a multi-context
attention mechanism into an end-to-end framework for human pose estimation. We adopt …