Data-driven Garment Pattern Estimation from 3D Geometries

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dc.contributor.authorGoto, Chihiroen_US
dc.contributor.authorUmetani, Nobuyukien_US
dc.contributor.editorTheisel, Holger and Wimmer, Michaelen_US
dc.date.accessioned2021-04-09T18:20:20Z
dc.date.available2021-04-09T18:20:20Z
dc.date.issued2021
dc.description.abstractThree-dimensional scanning technology recently becomes widely available to the public. However, it is difficult to simulate clothing deformation from the scanned people because scanned data lacks information required for the clothing simulation. In this paper, we present a technique to estimate clothing patterns from a scanned person in cloth. Our technique uses image-based deep learning to estimate the type of pattern on the projected image. The key contribution is converting image-based inference into three-dimensional clothing pattern estimation. We evaluate our technique by applying our technique to an actual scan.en_US
dc.description.sectionheadersModeling and Rendering
dc.description.seriesinformationEurographics 2021 - Short Papers
dc.identifier.doi10.2312/egs.20211013
dc.identifier.isbn978-3-03868-133-5
dc.identifier.issn1017-4656
dc.identifier.pages17-20
dc.identifier.urihttps://doi.org/10.2312/egs.20211013
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egs20211013
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectShape modeling
dc.subjectNeural networks
dc.titleData-driven Garment Pattern Estimation from 3D Geometriesen_US
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