Automatic generation of statistical shape models in motion

Sofia Scataglini; Robby Haelterman; Damien Van Tiggelen

doi:10.1007/978-3-319-94223-0_16

Automatic generation of statistical shape models in motion

Sofia Scataglini, Robby Haelterman, Damien Van Tiggelen

Hyperspectral Unit

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

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.

Originalsprache	Englisch
Titel	Advances in Human Factors in Simulation and Modeling - Proceedings of the AHFE 2018 International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization
Redakteure/-innen	Daniel N. Cassenti
Herausgeber (Verlag)	Springer
Seiten	170-178
Seitenumfang	9
ISBN (Print)	9783319942223
DOIs	https://doi.org/10.1007/978-3-319-94223-0_16
Publikationsstatus	Veröffentlicht - 2019
Veranstaltung	AHFE International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization, 2018 - Orlando, USA/Vereinigte Staaten Dauer: 21 Juli 2018 → 25 Juli 2018

Publikationsreihe

Name	Advances in Intelligent Systems and Computing
Band	780
ISSN (Print)	2194-5357

Konferenz

Konferenz	AHFE International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization, 2018
Land/Gebiet	USA/Vereinigte Staaten
Ort	Orlando
Zeitraum	21/07/18 → 25/07/18

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10.1007/978-3-319-94223-0_16

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Scataglini, S., Haelterman, R., & Van Tiggelen, D. (2019). Automatic generation of statistical shape models in motion. in D. N. Cassenti (Hrsg.), Advances in Human Factors in Simulation and Modeling - Proceedings of the AHFE 2018 International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization (S. 170-178). (Advances in Intelligent Systems and Computing; Band 780). Springer. https://doi.org/10.1007/978-3-319-94223-0_16

Scataglini, Sofia ; Haelterman, Robby ; Van Tiggelen, Damien. / Automatic generation of statistical shape models in motion. Advances in Human Factors in Simulation and Modeling - Proceedings of the AHFE 2018 International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization. Hrsg. / Daniel N. Cassenti. Springer, 2019. S. 170-178 (Advances in Intelligent Systems and Computing).

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title = "Automatic generation of statistical shape models in motion",

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.",

keywords = "Motion capturing, Shape prediction, Statistical body shape model",

author = "Sofia Scataglini and Robby Haelterman and {Van Tiggelen}, Damien",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2019.; AHFE International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization, 2018 ; Conference date: 21-07-2018 Through 25-07-2018",

year = "2019",

doi = "10.1007/978-3-319-94223-0_16",

language = "English",

isbn = "9783319942223",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "170--178",

editor = "Cassenti, {Daniel N.}",

booktitle = "Advances in Human Factors in Simulation and Modeling - Proceedings of the AHFE 2018 International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization",

}

Scataglini, S, Haelterman, R & Van Tiggelen, D 2019, Automatic generation of statistical shape models in motion. in DN Cassenti (Hrsg.), Advances in Human Factors in Simulation and Modeling - Proceedings of the AHFE 2018 International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization. Advances in Intelligent Systems and Computing, Bd. 780, Springer, S. 170-178, AHFE International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization, 2018, Orlando, USA/Vereinigte Staaten, 21/07/18. https://doi.org/10.1007/978-3-319-94223-0_16

Automatic generation of statistical shape models in motion. / Scataglini, Sofia; Haelterman, Robby; Van Tiggelen, Damien.
Advances in Human Factors in Simulation and Modeling - Proceedings of the AHFE 2018 International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization. Hrsg. / Daniel N. Cassenti. Springer, 2019. S. 170-178 (Advances in Intelligent Systems and Computing; Band 780).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Automatic generation of statistical shape models in motion

AU - Scataglini, Sofia

AU - Haelterman, Robby

AU - Van Tiggelen, Damien

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2019.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Motion capturing

KW - Shape prediction

KW - Statistical body shape model

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U2 - 10.1007/978-3-319-94223-0_16

DO - 10.1007/978-3-319-94223-0_16

M3 - Conference contribution

AN - SCOPUS:85053600995

SN - 9783319942223

T3 - Advances in Intelligent Systems and Computing

SP - 170

EP - 178

BT - Advances in Human Factors in Simulation and Modeling - Proceedings of the AHFE 2018 International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization

A2 - Cassenti, Daniel N.

PB - Springer

T2 - AHFE International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization, 2018

Y2 - 21 July 2018 through 25 July 2018

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Scataglini S, Haelterman R, Van Tiggelen D. Automatic generation of statistical shape models in motion. in Cassenti DN, Hrsg., Advances in Human Factors in Simulation and Modeling - Proceedings of the AHFE 2018 International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization. Springer. 2019. S. 170-178. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-94223-0_16

Automatic generation of statistical shape models in motion

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