Improving pose estimation by building dedicated datasets and using orientation

Markerless pose estimation systems are useful for various applications including human-
computer interaction, activity recognition, security, gait analysis, and computer-assisted medical interventions. They have attracted much interest since the release of low-cost depth
cameras such as Microsoft’s Kinect camera. Shotton et al. and Girshick et al. pioneered
tractable methods that infer a full-body pose reconstruction in real-time.
Despite this technological breakthrough, the accuracy of human pose estimation from single
depth images remains insufficient for some applications. Our work aims at building a simulation environment to create images databases suited for any camera position and improving
the mainstream machine learning-based pose estimation algorithms.