Data-driven Glove Calibration for Hand Motion Capture

Abstract

Hand motion is an important component of human motion, playing a central role in communication. However, it is difficult to capture hand motion optically, especially in conjunction with full body motion. Due to a lack of appropriate calibration methods, data gloves also do not provide sufficiently accurate hand motion. In this paper, we present a novel glove calibration approach that can map raw sensor readings to hand motion data with both accurate joint rotations and fingertip positions. Our method elegantly handles the sensor coupling problem by treating calibration as a flexible mapping from sensor readings to joint rotations. A sampling process collects data tuples according to accuracy requirements, and organizes all the tuples in a training set. From these data, a specially designed Gaussian Process Regression model is trained to infer the calibration function, and the learned model can be used to calibrate new sensor readings. For real-time hand motion capture, a sparse approximation of the model is used to enhance performance. Evaluation experiments demonstrate that our approach provides significantly better results that have more accurate hand shapes and fingertip positions, compared to other calibration methods.