3D human body modeling using range data

For the 3D modeling of walking humans the determination of body pose and extraction of body parts, from the sensed 3D range data, are challenging image processing problems. Real body data may have holes because of self-occlusions and grazing angle views. Most of the existing modeling methods rely on direct fitting a 3D model into the data without considering the fact that the parts in an image are indeed the human body parts. In this paper, we present a method for 3D human body modeling using range data that attempts to overcome these problems. In our approach the entire human body is first decomposed into major body parts by a parts-based image segmentation method, and then a kinematics model is fitted to the segmented body parts in an optimized manner. The fitted model is adjusted by the iterative closest point (ICP) algorithm to resolve the gaps in the body data. Experimental results and comparisons demonstrate the effectiveness of our approach.