Semantic Parametric Reshaping of Human Body Models

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

doi:10.1109/3DV.2014.47

Semantic Parametric Reshaping of Human Body Models

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

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

66 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 language	English
Title of host publication	Proceedings - 2014 International Conference on 3D Vision Workshops, 3DV 2014
Pages	41-48
Number of pages	8
ISBN (Electronic)	9781479970018
DOIs	https://doi.org/10.1109/3DV.2014.47
State	Published - Aug 7 2015
Event	2nd International Conference on 3D Vision Workshops, 3DV 2014 - Tokyo, Japan Duration: Dec 8 2014 → Dec 11 2014

Publication series

Name	Proceedings - 2014 International Conference on 3D Vision Workshops, 3DV 2014

Conference

Conference	2nd International Conference on 3D Vision Workshops, 3DV 2014
Country/Territory	Japan
City	Tokyo
Period	12/8/14 → 12/11/14

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

Access to Document

10.1109/3DV.2014.47

Cite this

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title = "Semantic Parametric Reshaping of Human Body Models",

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.",

keywords = "deformation, local mapping, reshaping, semantic parameters",

author = "Yipin Yang and Yao Yu and Yu Zhou and Sidan Du and James Davis and Ruigang Yang",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2nd International Conference on 3D Vision Workshops, 3DV 2014 ; Conference date: 08-12-2014 Through 11-12-2014",

year = "2015",

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doi = "10.1109/3DV.2014.47",

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Yang, Y, Yu, Y, Zhou, Y, Du, S, Davis, J & Yang, R 2015, Semantic Parametric Reshaping of Human Body Models. in Proceedings - 2014 International Conference on 3D Vision Workshops, 3DV 2014., 7182715, Proceedings - 2014 International Conference on 3D Vision Workshops, 3DV 2014, pp. 41-48, 2nd International Conference on 3D Vision Workshops, 3DV 2014, Tokyo, Japan, 12/8/14. https://doi.org/10.1109/3DV.2014.47

Semantic Parametric Reshaping of Human Body Models. / Yang, Yipin; Yu, Yao; Zhou, Yu et al.
Proceedings - 2014 International Conference on 3D Vision Workshops, 3DV 2014. 2015. p. 41-48 7182715 (Proceedings - 2014 International Conference on 3D Vision Workshops, 3DV 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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KW - local mapping

KW - reshaping

KW - semantic parameters

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Semantic Parametric Reshaping of Human Body Models

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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