Deep High-Resolution Representation Learning for Human Pose Estimation

02/25/2019
by   Ke Sun, et al.
0

This is an official pytorch implementation of Deep High-Resolution Representation Learning for Human Pose Estimation. In this work, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods recover high-resolution representations from low-resolution representations produced by a high-to-low resolution network. Instead, our proposed network maintains high-resolution representations through the whole process. We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel. We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high-resolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset. The code and models have been publicly available at <https://github.com/leoxiaobin/deep-high-resolution-net.pytorch>.

READ FULL TEXT
research
08/20/2019

Deep High-Resolution Representation Learning for Visual Recognition

High-resolution representations are essential for position-sensitive vis...
research
10/14/2019

Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation

Human pose estimation are of importance for visual understanding tasks s...
research
04/22/2022

Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation

A high-resolution network exhibits remarkable capability in extracting m...
research
03/20/2020

Multi-Person Pose Estimation with Enhanced Feature Aggregation and Selection

We propose a novel Enhanced Feature Aggregation and Selection network (E...
research
08/27/2019

Bottom-up Higher-Resolution Networks for Multi-Person Pose Estimation

In this paper, we are interested in bottom-up multi-person human pose es...
research
05/11/2022

AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation

Movement and pose assessment of newborns lets experienced pediatricians ...
research
07/07/2021

FasterPose: A Faster Simple Baseline for Human Pose Estimation

The performance of human pose estimation depends on the spatial accuracy...

Please sign up or login with your details

Forgot password? Click here to reset