Abstract. We present a new deep learning approach for matching deformable shapes by introducing Shape Deformation Networks which jointly
encode 3D shapes and correspondences. This is achieved by factoring
the surface representation into (i) a template, that parameterizes the
surface, and (ii) a learnt global feature vector that parameterizes the
transformation of the template into the input surface. By predicting this
feature for a new shape, we implicitly predict correspondences between
this shape and the template. We show that these correspondences can
be improved by an additional step which improves the shape feature by
minimizing the Chamfer distance between the input and transformed
template. We demonstrate that our simple approach improves on stateof-the-art results on the difficult FAUST-inter challenge, with an average
correspondence error of 2.88cm. We show, on the TOSCA dataset, that
our method is robust to many types of perturbations, and generalizes
to non-human shapes. This robustness allows it to perform well on real
unclean, meshes from the the SCAPE dataset