We present a novel motion-based approach for the part determination and shape estimation of a human's body parts. The novelty of the technique is that neither a prior model of the human body is employed nor prior body part segmentation is assumed. We present a human body part identification strategy (HBPIS) that recovers all the body parts of a moving human based on the spatiotemporal analysis of its deforming silhouette. We formalize the process of simultaneous part determination and 2D shape estimation by employing the supervisory control theory of discrete event systems. In addition, in order to acquire the 3D shape of the body parts, we present a new algorithm which selectively integrates the (segmented by the HBPIS) apparent contours, from three mutually orthogonal views. The effectiveness of the approach is demonstrated through a series of experiments, where a subject performs a set of movements according to a protocol that reveals the structure of the human body.