Posture normalization of 3D body scans

For product developers that design near-body products, virtual mannequins that represent realistic body shapes, are valuable tools. With statistical shape modeling, the variability of such body shapes can be described. Shape variation captured by statistical shape models (SSMs) is often polluted by posture variations, leading to less compact models. In this paper, we propose a framework that has low computational complexity to build a posture invariant SSM, by capturing and correcting the posture of an instance. The posture-normalized SSM is shown to be substantially more compact than the non-posture-normalized SSM. Practitioner Summary: Statistical shape modeling is a technique to map out the variability of (body) shapes. This variability is often polluted by variations in posture. In this paper, we propose a framework to build a posture invariant statistical shape model.

Publication year:2019

Keywords:A1 Journal article

BOF-keylabel:yes

BOF-publication weight:1

CSS-citation score:1

Authors:International

Authors from:Higher Education

Accessibility:Open