Publication
Title
Automatic generation of statistical shape models in motion
Author
Abstract
Statistical body shape modeling (SBSM) is a well-known technique to map out the variability of body shapes and is commonly used in 3D anthropometric analyses. In this paper, a new approach to integrate movement acquired by a motion capture system with a body shape is proposed. This was done by selecting landmarks on a body shape model, and predicting a body shape based on features. Then, a virtual skeleton was generated relative to those landmarks. This skeleton was parented to a body shape, allowing to modify its pose and to add pre-recorded motion to different body shapes in a realistic way.
Language
English
Source (book)
Advances in Human Factors in Simulation and Modeling. AHFE 2018. / Cassenti, D.N. [edit.]
Source (series)
Advances in Intelligent Systems and Computing; 780
Publication
Cham : Springer , 2019
ISSN
2194-5357
ISBN
978-3-319-94222-3
978-3-319-94223-0
Volume/pages
p. 170-178
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Project info
SHASIZE: a predictive tool based on statistical shape modeling for accurate clothing size prediction.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identification
Creation 26.10.2018
Last edited 15.07.2021
To cite this reference