Estimation of human body shape and posture under clothing

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DOIResolve DOI: http://doi.org/10.1016/j.cviu.2014.06.012
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TypeArticle
Journal titleComputer Vision and Image Understanding
ISSN1077-3142
Volume127
Pages3142; # of pages: 12
SubjectVirtual reality; Fitting accuracy; Geometry processing; Human subjects; Motion sequences; Posture modeling; Search spaces; Statistical modeling; Statistical shapes; Hosiery manufacture
AbstractEstimating the body shape and posture of a dressed human subject in motion represented as a sequence of (possibly incomplete) 3D meshes is important for virtual change rooms and security. To solve this problem, statistical shape spaces encoding human body shape and posture variations are commonly used to constrain the search space for the shape estimate. In this work, we propose a novel method that uses a posture-invariant shape space to model body shape variation combined with a skeleton-based deformation to model posture variation. Our method can estimate the body shape and posture of both static scans and motion sequences of human body scans with clothing that fits relatively closely to the body. In case of motion sequences, our method takes advantage of motion cues to solve for a single body shape estimate along with a sequence of posture estimates. We apply our approach to both static scans and motion sequences and demonstrate that using our method, higher fitting accuracy is achieved than when using a variant of the popular SCAPE model [2,18] as statistical model.
Publication date
PublisherElsevier
LanguageEnglish
AffiliationNational Research Council Canada; Information and Communication Technologies
Peer reviewedYes
NPARC number21272789
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Record identifier5919b8ca-dc39-45e4-ac12-e2ed2754a790
Record created2014-12-03
Record modified2016-05-09
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