Clustering of the Blendshape Facial Model

TL;DR

This paper introduces a novel unsupervised clustering approach for blendshape facial models that improves inverse rig parameter estimation accuracy while reducing computational costs by segmenting face deformations in mesh and controller spaces.

Contribution

It presents a two-fold clustering method focusing on deformation spaces, enabling independent modeling of face segments for better inverse rig estimation.

Findings

Enhanced accuracy in inverse rig parameter estimation.

Reduced computational cost compared to existing methods.

Segments correspond to meaningful facial regions and controls.

Abstract

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.