Collecting motion data is an important tool in controlling robots. Traditional approaches of motion capture usually use labels for passive markers. They suffers from several problems such as occlusions or cumbersome equipments. In the past few years, methods of markerless, unconstrained posture estimation using only cameras has received much attention from computer vision researchers. One of these methods is volume-based approach. Instead of deriving kinematic models directly from 2D images, this method first builds an intermediate 3D volume feature of the capture subject. Then fit a 3D body model is to the volume data. Here we proposed an approach for model-free, markerless, volume-based motion capture of humans. It is centered on generating underlying nonlinear axes (or a skeleton curve) from a volume of a human subject captured from multiple calibrated cameras.