Learning a Correlated Model of Identity and Pose-Dependent Body Shape Variation for Real-Time Synthesis
Brett Allen, Brian Curless, Zoran Popović, Aaron Hertzmann
Symposium on Computer Animation, September 2006, pp. 147--156.
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.
@inproceedings{Allen:2006:LAC,
author = {Brett Allen and Brian Curless and Zoran Popović and Aaron Hertzmann},
title = {Learning a Correlated Model of Identity and Pose-Dependent Body Shape Variation for Real-Time Synthesis},
booktitle = {Symposium on Computer Animation},
pages = {147--156},
month = sep,
year = {2006},
}
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