Predicting Realistic and Precise Human Body Models Under Clothing Based on Orthogonal-view Photos

Shuaiyin Zhu, Pik Yin Mok

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

The already difficult research problem becomes more challenging if the individual subjects to be modelled are dressed in normal or loose-fit clothing. In this study, we present an intelligent two-phase method to customize 3D digital human body models based on two orthogonal-view photos of the customers. It integrates both image-based and example-based modelling techniques to create human body models for individual customers with precise body measurements and realistic appearance. It fills up the research gap of human model customization; without the need of taking body scan, any customers can create their 3D digital body models only based on their orthogonal-view photos in normal or loose-fit clothing. Experimental results have shown that the proposed method can efficiently and accurately customize human models of diverse shapes, meeting the specific needs of the clothing industry.
Original languageEnglish
Pages (from-to)3812-3819
Number of pages8
JournalProcedia Manufacturing
Volume3
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • Artificial neural networks
  • Computer graphics
  • Deformation technology
  • Human body modelling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Industrial and Manufacturing Engineering

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