3D human pose estimation using part affinity field

Abstract

Nowadays, following the success of deep learning in the Computer Vision field, many research studies are underway to produce state-of-the-art technologies that can predict 3D human poses given raw image pixels. These end-to-end systems create possibilities for future studies such as human pose or gait recognition, and their practical values in industry are beyond imagination. This thesis proposes an end-to-end system that predicts human joint locations in 3D space using only the raw image pixels as inputs. While the most used state-of-the-art method proposes that lifting joint locations from camera space to 3D space can be done in a simple and effective way only using 2D joint locations as inputs, our proposed system is even more effective and accurate with the help of part affinity fields.