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 …

Person identification is a problem that has received substantial attention, particularly in
security domains. Gait recognition is one of the most convenient approaches enabling …

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 …

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 …

We present an approach to efficiently detect the 2D pose of multiple people in an image. The
approach uses a nonparametric representation, which we refer to as Part Affinity Fields …

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 …

Pose Machines provide a sequential prediction framework for learning rich implicit spatial
models. In this work we show a systematic design for how convolutional networks can be …

The goal of this paper is to advance the state-of-the-art of articulated pose estimation in
scenes with multiple people. To that end we contribute on three fronts. We propose (1) …

In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose estimation
architecture that combines a multi-task model with a novel assignment method …