Example-based Body Model Optimization and Skinning

Abstract

In this paper, we present an example-based framework for the generation of a realistic kinematic 3D human body model that optimizes shape, pose and skinning parameters. For enhanced realism, the skinning is realized as a combination of Linear Blend Skinning (LBS) and Dual quaternion Linear Blending (DLB) which nicely compensates the deficiencies of using only one of these approaches (e.g. candy wrapper, bulging artifacts) and supports interpolation of more than two joint transformations. The optimization framework enforces two objectives: resembling both shape and pose as closely as possible by iteratively minimizing the objective function with respect to (a) the vertices, (b) the skinning weights and (c) the joint parameters. Smoothness is ensured by using a weighted Laplacian besides a typical data term in the objective function, which introduces the only parameter to be specified. With experimental results on publicly available datasets we demonstrate the effectiveness of the resulting shape model, exposing convincing naturalism. By using examples for the optimization of all parameters, our framework is easy to use and does not require sophisticated parameter tuning or user intervention.