Learning a correlated model of identity and pose-dependent body shape variation for real-time synthesis

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. 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 1.