A Data-Driven Approach for 3D Human Body Pose Reconstruction from a Kinect Sensor

Abstract

In the study of virtual fitting techniques, human body modeling has always occupied a very important position. Whether a model that is roughly consistent with users can be established has a direct impact on the fitting experience of users. Based on this, we propose an automatic human body pose registration algorithm which can efficiently construct a posed model using priori data and a single Kinect sensor, and provide a good foundation for the later shape registration. Because of the complexity of the human body, there are so many methods that just reconstruct a human body model but no rigged animation skeleton inside. To solve this, we use SMPL which is a recently published statistical body model to fit 3D joints acquired by a Kinect sensor. And finally we project the posed model to the corresponding person in the color image to improve the fitting experience. Our experiments show that the speed and the estimation error of the algorithm are within the tolerance of virtual fitting.