  Registration of 3D human body has been a challenging research topic for over
decades. Most of the traditional human body registration methods require manual
assistance, or other auxiliary information such as texture and markers. The
majority of these methods are tailored for high-quality scans from expensive
scanners. Following the introduction of the low-quality scans from
cost-effective devices such as Kinect, the 3D data capturing of human body
becomes more convenient and easier. However, due to the inevitable holes,
noises and outliers in the low-quality scan, the registration of human body
becomes even more challenging. To address this problem, we propose a fully
automatic active registration method which deforms a high-resolution template
mesh to match the low-quality human body scans. Our registration method
operates on two levels of statistical shape models: (1) the first level is a
holistic body shape model that defines the basic figure of human; (2) the
second level includes a set of shape models for every body part, aiming at
capturing more body details. Our fitting procedure follows a coarse-to-fine
approach that is robust and efficient. Experiments show that our method is
comparable with the state-of-the-art methods.
