Two-layer sparse compression of dense-weight blend skinning

Weighted linear interpolation has been widely used in many skinning techniques including linear blend skinning, dual quaternion blend skinning, and cage based deformation. To speed up performance, these skinning models typically employ a sparseness constraint, in which each 3D model vertex has a small fixed number of non-zero weights. However, the sparseness constraint also imposes certain limitations to skinning models and their various applications. This paper introduces an efficient two-layer sparse compression technique to substantially reduce the computational cost of a dense-weight skinning model, with insignificant loss of its visual quality. It can directly work on dense skinning weights or use example-based skinning decomposition to further improve its accuracy. Experiments and comparisons demonstrate that the introduced sparse compression model can significantly outperform state of the art weight reduction algorithms, as well as skinning decomposition algorithms with a sparseness constraint.