Semantic Parametric Reshaping of Human Body Models

Yipin Yang, Yao Yu, Yu Zhou, Sidan Du, James Davis, Ruigang Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

65 Scopus citations

Abstract

We develop a novel approach to generate human body models in a variety of shapes and poses via tuning semantic parameters. Our approach is investigated with datasets of up to 3000 scanned body models which have been placed in point to point correspondence. Correspondence is established by nonrigid deformation of a template mesh. The large dataset allows a local model to be learned robustly, in which individual parts of the human body can be accurately reshaped according to semantic parameters. We evaluate performance on two datasets and find that our model outperforms existing methods.

Original languageEnglish
Title of host publicationProceedings - 2014 International Conference on 3D Vision Workshops, 3DV 2014
Pages41-48
Number of pages8
ISBN (Electronic)9781479970018
DOIs
StatePublished - Aug 7 2015
Event2nd International Conference on 3D Vision Workshops, 3DV 2014 - Tokyo, Japan
Duration: Dec 8 2014Dec 11 2014

Publication series

NameProceedings - 2014 International Conference on 3D Vision Workshops, 3DV 2014

Conference

Conference2nd International Conference on 3D Vision Workshops, 3DV 2014
Country/TerritoryJapan
CityTokyo
Period12/8/1412/11/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

  • deformation
  • local mapping
  • reshaping
  • semantic parameters

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Radiology Nuclear Medicine and imaging

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