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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 identity-space, 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.

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