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Learning a Correlated Model of Identity and Pose-Dependent Body Shape Variation for Real-Time Synthesis
Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation - SCA '04
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 thedoi:10.2312/sca/sca06/147-156 fatcat:bmudvco4pnamrjjsflmqwze3eu