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dc.contributor.authorBender, Janen_US
dc.contributor.authorDeul, Crispinen_US
dc.contributor.editorJan Bender and Arjan Kuijper and Dieter W. Fellner and Eric Guerinen_US
dc.date.accessioned2013-11-08T10:53:59Z
dc.date.available2013-11-08T10:53:59Z
dc.date.issued2012en_US
dc.identifier.isbn978-3-905673-96-8en_US
dc.identifier.urihttp://dx.doi.org/10.2312/PE/vriphys/vriphys12/021-030en_US
dc.description.abstractIn this paper we present an efficient adaptive cloth simulation based on the sqrt-3-refinement scheme. Our adaptive cloth model can handle arbitrary triangle meshes and is not restricted to regular grid meshes which are required by other methods. Previous works on adaptive cloth simulation often use discrete cloth models like mass-spring systems in combination with a specific subdivision scheme. The problem of such models is that the simulation does not converge to the correct solution as the mesh is refined. We propose to use a cloth model which is based on continuum mechanics since continuous models do not have this problem. In order to perform an efficient simulation we use a linear elasticity model in combination with a corotational formulation. The sqrt-3-subdivision scheme has the advantage that it generates high quality meshes while the number of triangles increases only by a factor of 3 in each refinement step. However, the original scheme only defines a mesh refinement. Therefore, we introduce an extension to support the coarsening of our simulation model as well. Our proposed mesh adaption can be performed efficiently and therefore does not cause much overhead. In this paper we will show that a significant performance gain can be achieved by our adaptive method.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectComputer Graphics [I.3.7]en_US
dc.subjectThree Dimensional Graphics and Realismen_US
dc.subjectAnimationen_US
dc.titleEfficient Cloth Simulation Using an Adaptive Finite Element Methoden_US
dc.description.seriesinformationWorkshop on Virtual Reality Interaction and Physical Simulationen_US


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