An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge

09/17/2018
by   Xiao Sun, et al.
0

For the ECCV 2018 PoseTrack Challenge, we present a 3D human pose estimation system based mainly on the integral human pose regression method. We show a comprehensive ablation study to examine the key performance factors of the proposed system. Our system obtains 47mm MPJPE on the CHALL_H80K test dataset, placing second in the ECCV2018 3D human pose estimation challenge. Code will be released to facilitate future work.

READ FULL TEXT
research
10/03/2018

Cascaded Pyramid Network for 3D Human Pose Estimation Challenge

Over the past decade, there has been a growing interest in human pose es...
research
08/18/2019

On the Robustness of Human Pose Estimation

This paper provides, to the best of our knowledge, the first comprehensi...
research
11/29/2017

DeepSkeleton: Skeleton Map for 3D Human Pose Regression

Despite recent success on 2D human pose estimation, 3D human pose estima...
research
11/22/2017

Integral Human Pose Regression

State-of-the-art human pose estimation methods are dominated by complex ...
research
07/23/2021

Human Pose Regression with Residual Log-likelihood Estimation

Heatmap-based methods dominate in the field of human pose estimation by ...
research
08/25/2022

FusePose: IMU-Vision Sensor Fusion in Kinematic Space for Parametric Human Pose Estimation

There exist challenging problems in 3D human pose estimation mission, su...
research
05/15/2021

Composite Localization for Human Pose Estimation

The existing human pose estimation methods are confronted with inaccurat...

Please sign up or login with your details

Forgot password? Click here to reset