Sparse representation and convolutional neural networks for 3D human pose estimation

Abstract

In the field of 3D Human Pose Estimation and Reconstruction based on body joints extracted from a 2D image; Exists challenges like self-occlusion and depth perception. These problems hinder approximate estimations. This article proposes a hybrid method that consists of semantic segmentation, sparse representation and 2D pose estimation for 3D estimation. Specifically, we train two fully Convolution neural networks (FCNs) to estimate 2D pose and semantic segmentation. Then, we narrow the estimation down using basic human body structure and results from the FCNs. Subsequently, utilizing the estimated 2D pose of the earlier step and a sparse representation model the 3D pose will estimate. Using a Convolution Neural Networks learning method for 2D human pose estimation and then by sparse representation will estimate. Results of the proposed method, show improvement relative to the previous works on 3D Human pose estimation. 3D Human Pose Estimation by this method demonstrates a significant mean error reduction compared to earlier studies.