Depth image based 3D hand pose estimation framework

Abstract

Real-time 3D motion capture for the human hand opens many avenues for HCI. This work describes our framework for fitting a 3D skeleton to the human hand using depth images. We represent a human hand by a 3D skeleton with 15 joints. Using this model, various synthetic depth images are generated. Random Decision Forests (RDF) are trained and used to assign each pixel to a hand part. A mean-shift method is used for estimating joint locations using pixel classification results. Our system runs in real time at 30 fps on Kinect depth images.