# Clustering of the Blendshape Facial Model

arXiv: https://arxiv.org/abs/2110.15313 · HTML: https://mlsanity.com/paper/2110.15313/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 18 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Built on by (most cited first, 4 of 4)

- [Neural Face Skinning for Mesh-agnostic Facial Expression Cloning](https://mlsanity.com/paper/2505.22416/) (2025)
- [High-fidelity Interpretable Inverse Rig: An Accurate and Sparse Solution Optimizing the Quartic Blendshape Model](https://mlsanity.com/paper/2302.04820/) (2023)
- [Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data](https://mlsanity.com/paper/2203.14113/) (2022)
- [Distributed Solution of the Inverse Rig Problem in Blendshape Facial Animation](https://mlsanity.com/paper/2303.06370/) (2023)

## Abstract (arXiv)

Digital human animation relies on high-quality 3D models of the human face -- rigs. A face rig must be accurate and, at the same time, fast to compute. One of the most common rigging models is the blendshape model. We present a novel approach for learning the inverse rig parameters at increased accuracy and decreased computational cost at the same time. It is based on a two-fold clustering of the blendshape face model. Our method focuses exclusively on the underlying space of deformation and produces clusters in both the mesh space and the controller space -- something that was not investigated in previous literature. This segmentation finds intuitive and meaningful connections between groups of vertices on the face and deformation controls, and further these segments can be observed independently. A separate model for solving the inverse rig problem is then learned for each segment. Our method is completely unsupervised and highly parallelizable.