  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.
