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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Shape from Selfies: Human Body Shape Estimation Using CCA Regression Forests

verfasst von : Endri Dibra, Cengiz Öztireli, Remo Ziegler, Markus Gross

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

In this work, we revise the problem of human body shape estimation from monocular imagery. Starting from a statistical human shape model that describes a body shape with shape parameters, we describe a novel approach to automatically estimate these parameters from a single input shape silhouette using semi-supervised learning. By utilizing silhouette features that encode local and global properties robust to noise, pose and view changes, and projecting them to lower dimensional spaces obtained through multi-view learning with canonical correlation analysis, we show how regression forests can be used to compute an accurate mapping from the silhouette to the shape parameter space. This results in a very fast, robust and automatic system under mild self-occlusion assumptions. We extensively evaluate our method on thousands of synthetic and real data and compare it to the state-of-art approaches that operate under more restrictive assumptions.

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Vorheriges Kapitel From Multiview Image Curves to 3D Drawings
Nächstes Kapitel Can We Jointly Register and Reconstruct Creased Surfaces by Shape-from-Template Accurately?
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Metadaten
Titel
Shape from Selfies: Human Body Shape Estimation Using CCA Regression Forests
verfasst von
Endri Dibra
Cengiz Öztireli
Remo Ziegler
Markus Gross
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46492-3

Electronic ISBN: 978-3-319-46493-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46493-0_6