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Two-Layer Sparse Compression of Dense-Weight Blend Skinning
Compact two-layer representation for dense skinning weights enabling efficient compression while preserving deformation quality.
Abstract
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.
How to read this
- Category
- Method: compression of dense blend-skinning weights
- Contributions
- A two-layer sparse compression technique that substantially reduces the cost of dense-weight skinning with little visual-quality loss
- Works directly on dense weights or via example-based skinning decomposition for improved accuracy
- Reported to outperform state-of-the-art weight-reduction and sparse skinning-decomposition methods
- Context
- Applies broadly to weighted-interpolation skinning (linear blend, dual quaternion, cage-based) and builds on the authors' Smooth Skinning Decomposition with Rigid Bones.Builds on: Smooth Skinning Decomposition with Rigid Bones
- Correctness
- Premised on the idea that dense weights can be factored into two sparse layers with insignificant visual error; comparisons are against existing weight-reduction and decomposition baselines, so gains are relative to those and the 'insignificant loss' claim is a visual-quality judgment.
- Clarity
- Clear problem statement; a first pass conveys why sparseness constraints limit skinning, a second pass is needed for the two-layer factorization math.
- How to read it
- Focus on what the two layers represent and how they replace a single sparse weight set; a second pass pays off for the decomposition formulation and the comparison setup.
Builds on
Built upon by
Nothing yet.
Related work
- Smooth Skinning Decomposition with Rigid Bones 2012 / SIGGRAPH Asia
- Direct Delta Mush Skinning Compression with Continuous Examples 2021 / SIGGRAPH
- Robust and Accurate Skeletal Rigging from Mesh Sequences 2014 / SIGGRAPH
- Skinning with Dual Quaternions 2007 / SIGGRAPH
Keywords
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