  We introduce the first completely unsupervised correspondence learning
approach for deformable 3D shapes. Key to our model is the understanding that
natural deformations (such as changes in pose) approximately preserve the
metric structure of the surface, yielding a natural criterion to drive the
learning process toward distortion-minimizing predictions. On this basis, we
overcome the need for annotated data and replace it by a purely geometric
criterion. The resulting learning model is class-agnostic, and is able to
leverage any type of deformable geometric data for the training phase. In
contrast to existing supervised approaches which specialize on the class seen
at training time, we demonstrate stronger generalization as well as
applicability to a variety of challenging settings. We showcase our method on a
wide selection of correspondence benchmarks, where we outperform other methods
in terms of accuracy, generalization, and efficiency.
