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Conference Paper: Real-time data driven deformation using kernel canonical correlation analysis

TitleReal-time data driven deformation using kernel canonical correlation analysis
Authors
KeywordsAnimation
Poisson Equation
Regression
Skinning
Issue Date2008
Citation
Siggraph'08: International Conference On Computer Graphics And Interactive Techniques, Acm Siggraph 2008 Papers 2008, 2008 How to Cite?
Abstract
Achieving intuitive control of animated surface deformation while observing a specific style is an important but challenging task in computer graphics. Solutions to this task can find many applications in data-driven skin animation, computer puppetry, and computer games. In this paper, we present an intuitive and powerful animation interface to simultaneously control the deformation of a large number of local regions on a deformable surface with a minimal number of control points. Our method learns suitable deformation subspaces from training examples, and generate new deformations on the fly according to the movements of the control points. Our contributions include a novel deformation regression method based on kernel Canonical Correlation Analysis (CCA) and a Poisson-based translation solving technique for easy and fast deformation control based on examples. Our run-time algorithm can be implemented on GPUs and can achieve a few hundred frames per second even for large datasets with hundreds of training examples. © 2008 ACM.
Persistent Identifierhttp://hdl.handle.net/10722/151936
References

 

Author Affiliations
  1. University of Illinois at Urbana-Champaign
DC FieldValueLanguage
dc.contributor.authorFeng, WWen_US
dc.contributor.authorKim, BUen_US
dc.contributor.authorYu, Yen_US
dc.date.accessioned2012-06-26T06:31:12Z-
dc.date.available2012-06-26T06:31:12Z-
dc.date.issued2008en_US
dc.identifier.citationSiggraph'08: International Conference On Computer Graphics And Interactive Techniques, Acm Siggraph 2008 Papers 2008, 2008en_US
dc.identifier.urihttp://hdl.handle.net/10722/151936-
dc.description.abstractAchieving intuitive control of animated surface deformation while observing a specific style is an important but challenging task in computer graphics. Solutions to this task can find many applications in data-driven skin animation, computer puppetry, and computer games. In this paper, we present an intuitive and powerful animation interface to simultaneously control the deformation of a large number of local regions on a deformable surface with a minimal number of control points. Our method learns suitable deformation subspaces from training examples, and generate new deformations on the fly according to the movements of the control points. Our contributions include a novel deformation regression method based on kernel Canonical Correlation Analysis (CCA) and a Poisson-based translation solving technique for easy and fast deformation control based on examples. Our run-time algorithm can be implemented on GPUs and can achieve a few hundred frames per second even for large datasets with hundreds of training examples. © 2008 ACM.en_US
dc.languageengen_US
dc.relation.ispartofSIGGRAPH'08: International Conference on Computer Graphics and Interactive Techniques, ACM SIGGRAPH 2008 Papers 2008en_US
dc.subjectAnimationen_US
dc.subjectPoisson Equationen_US
dc.subjectRegressionen_US
dc.subjectSkinningen_US
dc.titleReal-time data driven deformation using kernel canonical correlation analysisen_US
dc.typeConference_Paperen_US
dc.identifier.emailYu, Y:yzyu@cs.hku.hken_US
dc.identifier.authorityYu, Y=rp01415en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-57649110690en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-57649110690&selection=ref&src=s&origin=recordpageen_US
dc.identifier.scopusauthoridFeng, WW=36960295400en_US
dc.identifier.scopusauthoridKim, BU=24537622900en_US
dc.identifier.scopusauthoridYu, Y=8554163500en_US

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