  The proliferation of 3D scanning technology has driven a need for methods to
interpret geometric data, particularly for human subjects. In this paper we
propose an elegant fusion of regression (bottom-up) and generative (top-down)
methods to fit a parametric template model to raw scan meshes.
  Our first major contribution is an intrinsic convolutional mesh U-net
architecture that predicts pointwise correspondence to a template surface.
Soft-correspondence is formulated as coordinates in a newly-constructed
Cartesian space. Modeling correspondence as Euclidean proximity enables
efficient optimization, both for network training and for the next step of the
algorithm.
  Our second contribution is a generative optimization algorithm that uses the
U-net correspondence predictions to guide a parametric Iterative Closest Point
registration. By employing pre-trained human surface parametric models we
maximally leverage domain-specific prior knowledge.
  The pairing of a mesh-convolutional network with generative model fitting
enables us to predict correspondence for real human surface scans including
occlusions, partialities, and varying genus (e.g. from self-contact). We
evaluate the proposed method on the FAUST correspondence challenge where we
achieve 20% (33%) improvement over state of the art methods for inter- (intra-)
subject correspondence.
