  We present a new technique named "Meta Deformation Network" for 3D shape
matching via deformation, in which a deep neural network maps a reference shape
onto the parameters of a second neural network whose task is to give the
correspondence between a learned template and query shape via deformation. We
categorize the second neural network as a meta-function, or a function
generated by another function, as its parameters are dynamically given by the
first network on a per-input basis. This leads to a straightforward overall
architecture and faster execution speeds, without loss in the quality of the
deformation of the template. We show in our experiments that Meta Deformation
Network leads to improvements on the MPI-FAUST Inter Challenge over designs
that utilized a conventional decoder design that has non-dynamic parameters.
