| dc.contributor.author | Allen, Brett | en_US |
| dc.contributor.author | Curless, Brian | en_US |
| dc.contributor.author | Popovic, Zoran | en_US |
| dc.contributor.author | Hertzmann, Aaron | en_US |
| dc.contributor.editor | Marie-Paule Cani and James O'Brien | en_US |
| dc.date.accessioned | 2014-01-29T07:24:48Z | |
| dc.date.available | 2014-01-29T07:24:48Z | |
| dc.date.issued | 2006 | en_US |
| dc.identifier.isbn | 3-905673-34-7 | en_US |
| dc.identifier.issn | 1727-5288 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.2312/SCA/SCA06/147-156 | en_US |
| dc.description.abstract | We present a method for learning a model of human body shape variation from a corpus of 3D range scans. Our model is the first to capture both identity-dependent and pose-dependent shape variation in a correlated fashion, enabling creation of a variety of virtual human characters with realistic and non-linear body deformations that are customized to the individual. Our learning method is robust to irregular sampling in pose-space and identityspace, and also to missing surface data in the examples. Our synthesized character models are based on standard skinning techniques and can be rendered in real time. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Curve, surface, solid and object modeling; I.3.7 [Computer Graphics]: Animation | en_US |
| dc.title | Learning a Correlated Model of Identity and Pose-Dependent Body Shape Variation for Real-Time Synthesis | en_US |
| dc.description.seriesinformation | ACM SIGGRAPH / Eurographics Symposium on Computer Animation | en_US |
