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Title:3D human pose estimation using part affinity field
Author(s):Zhao, Zixu
Contributor(s):Huang, Thomas S.
Subject(s):Computer Vision
Pose Estimation
Neural Network
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.
Issue Date:2018-05
Date Available in IDEALS:2018-05-30

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