Generating Synthetic Humans for Learning 3D Pose Estimation

PubDate: March 2019

Teams: Tokyo Institute of Technology

Writers: Kohei Aso; Dong-Hyun Hwang; Hideki Koike

PDF: Generating Synthetic Humans for Learning 3D Pose Estimation

Abstract

We generate synthetic annotated data for learning 3D human pose estimation using an egocentric fisheye camera. Synthetic humans are rendered from a virtual fisheye camera, with a random background, random clothing, random lighting parameters. In addition to RGB images, we generate ground truth of 2D/3D poses and location heat-maps. Capturing huge and various images and labeling manually for learning are not required. This approach will be used for the challenging situation such as capturing training data in sports.