Sampling-based Rig Conversion into Non-rigid Helper Bones

Abstract

While 3D animation packages provide a wide variety of animation rigs for creating expressive skin animation, most interactive systems employ linear blend skinning for hard realtime computation. We propose a method for converting an arbitrary skeleton-driven deformer into a linear blend skinning-based helper bone rig. Our system builds the target rig by applying an example-based skinning technique that uses a minimal training dataset obtained from the source model by two-pass sampling of the skin deformation. The first uniform sampling analyzes the relationship between the rotation of each joint and the deformation of skin vertices. The second sampling composes a minimum training dataset by selecting important pose samples using novel geometrical measures. We also propose a skinning decomposition with similarity transformation algorithm for accurately approximating the non-rigid skin deformation behavior by helper bone transformations. Our experimental results demonstrate the proposed automated rig conversion into non-rigid helper bones from several skeleton-driven deformers, including Delta Mush deformers, corrective blendshapes, and virtual-muscle systems.