Automatic Generation of Statistical Shape Models in Motion

Femke Danckaers, Sofia Scataglini, Robby Haelterman, Damien Van Tiggelen, Toon Huysmans, Jan Sijbers

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationAdvances in Human Factors in Simulation and Modeling
Subtitle of host publicationProceedings of the AHFE 2018 International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization
EditorsDaniel N. Cassenti
Place of PublicationCham
PublisherSpringer
Pages170-178
Number of pages9
ISBN (Electronic)978-3-319-94223-0
ISBN (Print)978-3-319-94222-3
DOIs
Publication statusPublished - 2019
EventAHFE 2018 : International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization - Orlando, United States
Duration: 21 Jul 201825 Jul 2018

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
Volume780
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceAHFE 2018
CountryUnited States
CityOrlando
Period21/07/1825/07/18

Keywords

  • Statistical body shape model
  • Motion capturing
  • Shape prediction

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