SkinCells: Sparse Skinning using Voronoi Cells

TL;DR

SkinCells introduces an automated, sparse skinning method that optimizes weights within a continuous volume, enabling high-quality, scalable character animation suitable for mobile environments.

Contribution

The paper presents SkinCells, a novel parameterized function family for automated skinning that controls sparsity and manages multiple levels of detail efficiently.

Findings

Produces stable, high-quality skinning weights in complex scenarios

Allows direct control over bone influences per vertex

Optimizes weights across multiple asset resolutions with a single pass

Abstract

For decades, real-time skinning has been the cornerstone of character animation in visual effects and games. Despite its importance, the creation of animatable digital assets remains a labor-intensive manual process. Existing automated tools frequently struggle with intricate geometries, often necessitating significant manual refinement to reach production standards. We present a robust, fully automated method for generating high-quality skinning weights from a standard mesh and skeleton in a canonical A- or T-pose. Unlike traditional approaches, our framework offers direct sparsity controls to limit bone influences per vertex -- a critical requirement for maintaining performance in large-scale mobile environments. Furthermore, we address the challenge of Level-of-Detail (LoD) management by optimizing weights within a continuous spatial volume rather than on discrete vertices. This allows a single optimization pass to be applied seamlessly across multiple asset resolutions and variations. Central to our approach is a novel parameterized family of functions, we call SkinCells. We demonstrate that our method consistently produces stable, high-quality results even in complex scenarios where standard biharmonic weight computations fail.