  We propose a new approach for 3D shape matching of deformable human shapes.
Our approach is based on the joint adoption of three different tools: an
intrinsic spectral matching pipeline, a morphable model, and an extrinsic
details refinement. By operating in conjunction, these tools allow us to
greatly improve the quality of the matching while at the same time resolving
the key issues exhibited by each tool individually. In this paper we present an
innovative High-Resolution Augmentation (HRA) strategy that enables highly
accurate correspondence even in the presence of significant mesh resolution
mismatch between the input shapes. This augmentation provides an effective
workaround for the resolution limitations imposed by the adopted morphable
model. The HRA in its global and localized versions represents a novel
refinement strategy for surface subdivision methods. We demonstrate the
accuracy of the proposed pipeline on multiple challenging benchmarks, and
showcase its effectiveness in surface registration and texture transfer.
