Learning a Correlated Model of Identity and Pose-Dependent Body Shape Variation for Real-Time Synthesis

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.