Two-stage Model Fitting Approach for Human Body Shape Estimation from a Single Depth Image

Mei Oyama, Naoshi Kaneko, Masaki Hayashi, Kazuhiko Sumi, Takeshi Yoshida

Abstract: Recovering an accurate 3D human body shape from a single depth image is one of the challenging problems in computer vision due to sensor noises, complexity of human body shapes, and variation of individual body shapes. In this paper, we address the problem using a two-stage model fitting approach. At the first stage, a coarse template model is fitted to the human pose of the input depth image using skeleton deformation. Then the model is fitted to the human shape by Laplacian surface editing. This fitting may corrupt the human-like shape of the template model due to the incompleteness of depth information. Then in the second stage, body shape details are recovered by fitting of a fine model to the deformed template by Stitched Puppet model fitting. Several experiments demonstrate that our approach recovers the most likely body shape of the input and deals with over a variety of input body shapes.