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dc.contributor.authorBrodt, Kirill
dc.contributor.authorBessmeltsev, Mikhail
dc.date.accessioned2023-09-13T12:05:42Z
dc.date.availableNO_RESTRICTIONfr
dc.date.available2023-09-13T12:05:42Z
dc.date.issued2022-07-22
dc.identifier.urihttp://hdl.handle.net/1866/28663
dc.publisherAssociation for computing machineryfr
dc.subjectCharacter posingfr
dc.subjectRigged and skinned charactersfr
dc.subjectSketch-based posingfr
dc.subjectCharacter sketchesfr
dc.titleSketch2Pose : estimating a 3D character pose from a bitmap sketchfr
dc.typeArticlefr
dc.contributor.affiliationUniversité de Montréal. Faculté des arts et des sciences. Département d'informatique et de recherche opérationnellefr
dc.identifier.doi10.1145/3528223.3530106
dcterms.abstractArtists frequently capture character poses via raster sketches, then use these drawings as a reference while posing a 3D character in a specialized 3D software --- a time-consuming process, requiring specialized 3D training and mental effort. We tackle this challenge by proposing the first system for automatically inferring a 3D character pose from a single bitmap sketch, producing poses consistent with viewer expectations. Algorithmically interpreting bitmap sketches is challenging, as they contain significantly distorted proportions and foreshortening. We address this by predicting three key elements of a drawing, necessary to disambiguate the drawn poses: 2D bone tangents, self-contacts, and bone foreshortening. These elements are then leveraged in an optimization inferring the 3D character pose consistent with the artist's intent. Our optimization balances cues derived from artistic literature and perception research to compensate for distorted character proportions. We demonstrate a gallery of results on sketches of numerous styles. We validate our method via numerical evaluations, user studies, and comparisons to manually posed characters and previous work.fr
dcterms.isPartOfurn:ISSN:0730-0301fr
dcterms.isPartOfurn:ISSN:1557-7368fr
dcterms.languageengfr
UdeM.ReferenceFournieParDeposantdoi/10.1145/3528223.3530106fr
UdeM.VersionRioxxVersion acceptée / Accepted Manuscriptfr
oaire.citationTitleACM Transactions on graphicsfr
oaire.citationVolume41fr
oaire.citationIssue4fr


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