Capture and Statistical Modeling of Arm-Muscle Deformations

Abstract

We present a comprehensive data-driven statistical model for skin and muscle deformation of the human shoulderarm complex. Skin deformations arise from complex bio-physical effects such as non-linear elasticity of muscles, fat, and connective tissue; and vary with physiological constitution of the subjects and external forces applied during motion. Thus, they are hard to model by direct physical simulation. Our alternative approach is based on learning deformations from multiple subjects performing different exercises under varying external forces. We capture the training data through a novel multi-camera approach that is able to reconstruct fine-scale muscle detail in motion. The resulting reconstructions from several people are aligned into one common shape parametrization, and learned using a semi-parametric non-linear method. Our learned data-driven model is fast, compact and controllable with a small set of intuitive parameters - pose, body shape and external forces, through which a novice artist can interactively produce complex muscle deformations. Our method is able to capture and synthesize fine-scale muscle bulge effects to a greater level of realism than achieved previously. We provide quantitative and qualitative validation of our method.