Using density-based clustering to improve skeleton embedding in the Pinocchio automatic rigging system

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dc.contributor.author Wang, Haolei
dc.date.accessioned 2012-11-28T17:36:53Z
dc.date.available 2012-11-28T17:36:53Z
dc.date.issued 2012-11-28
dc.identifier.uri http://hdl.handle.net/2097/15102
dc.description.abstract Automatic rigging is a targeting approach that takes a 3-D character mesh and an adapted skeleton and automatically embeds it into the mesh. Automating the embedding step provides a savings over traditional character rigging approaches, which require manual guidance, at the cost of occasional errors in recognizing parts of the mesh and aligning bones of the skeleton with it. In this thesis, I examine the problem of reducing such errors in an auto-rigging system and apply a density-based clustering algorithm to correct errors in a particular system, Pinocchio (Baran & Popovic, 2007). I show how the density-based clustering algorithm DBSCAN (Ester et al., 1996) is able to filter out some impossible vertices to correct errors at character extremities (hair, hands, and feet) and those resulting from clothing that hides extremities such as legs. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Computer graphics en_US
dc.subject Automatic rigging en_US
dc.subject Skeleton embedding en_US
dc.subject Character modeling en_US
dc.subject Clustering algorithms en_US
dc.title Using density-based clustering to improve skeleton embedding in the Pinocchio automatic rigging system en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Computing and Information Sciences en_US
dc.description.advisor William H. Hsu en_US
dc.subject.umi Computer Science (0984) en_US
dc.date.published 2012 en_US
dc.date.graduationmonth December en_US


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