Example-based statistical framework for parametric modeling of human body shapes

Abstract

This paper proposes a new example-based statistical modeling framework to conduct the parametric modeling of 3D human body shapes from linear anthropometric parameters. The modeling framework consists of the following three phases: construction of a training database of human body shapes, statistical analysis of human body shapes and human body shape modeling. In the training database construction phase, a consistent parameterization was carried out on 3D whole-body scan data of 80 males and 80 females with a wide variety of body shapes as the examples for this study. The surface-fitting process, which was improved relative to existing methods, was used to guarantee the high-quality parameterization in this phase and to generate an articulated body shape model in the modeling phase. To characterize the range of body shape variation, the training database was analyzed statistically. Additionally, a correlation between the body shapes and the anthropometric parameters was learned in the statistical analysis phase to estimate body shapes from intuitive and semantic parameters. A new technique to generate an optimal body shape model that precisely satisfies user input body dimensions was developed in the model generation phase. This technique enables the estimation of body shape variation, not only within the body shape space that was learned statistically, but also outside of the body shape space, while maintaining body shapes that stay in the human shape space. Our approach produced reasonable results having a high modeling accuracy satisfying user-specified anthropometric parameters, and high visual quality in expressing realistic body shapes. The resultant models were then segmented into 16 key regions of the human body, and had information on 15 key joints, and thus they could be a useful tool in various industries. Compared with other statistical modeling approaches, the proposed method contributes to related areas by introducing an improved surface-fitting process and a non-linear optimization-based optimal body shape modeling technique.