Individual hand model to reconstruct behavior from motion capture data

This paper proposes a method to build an individual hand model that consists of a surface skin and an inside link model, which can be used to reconstruct hand behavior from motion capture (MoCap) data. Our system uses a static posture data, a palmar side photo and marker positions captured simultaneously by MoCap, to reduce extra time and effort demanded for each subject to build a model. From this modeling scan, several hand dimensions and marker positions are obtained. Joint centers are estimated based on regression analysis about joint centers, marker positions and some hand dimensions derived from magnetic resonance (MR) images of eight subjects. The skin surface is built by scaling a generic hand model so that it satisfies the measured dimensions. The proposed system will be validated through an experiment to build four subjects´ hand models.