  Character rigging is universally needed in computer graphics but notoriously
laborious. We present a new method, HeterSkinNet, aiming to fully automate such
processes and significantly boost productivity. Given a character mesh and
skeleton as input, our method builds a heterogeneous graph that treats the mesh
vertices and the skeletal bones as nodes of different types and uses graph
convolutions to learn their relationships. To tackle the graph heterogeneity,
we propose a new graph network convolution operator that transfers information
between heterogeneous nodes. The convolution is based on a new distance
HollowDist that quantifies the relations between mesh vertices and bones. We
show that HeterSkinNet is robust for production characters by providing the
ability to incorporate meshes and skeletons with arbitrary topologies and
morphologies (e.g., out-of-body bones, disconnected mesh components, etc.).
Through exhaustive comparisons, we show that HeterSkinNet outperforms
state-of-the-art methods by large margins in terms of rigging accuracy and
naturalness. HeterSkinNet provides a solution for effective and robust
character rigging.
