  The animation community has spent significant effort trying to ease rigging
procedures. This is necessitated because the increasing availability of 3D data
makes manual rigging infeasible. However, object animations involve
understanding elaborate geometry and dynamics, and such knowledge is hard to
infuse even with modern data-driven techniques. Automatic rigging methods do
not provide adequate control and cannot generalize in the presence of unseen
artifacts. As an alternative, one can design a system for one shape and then
transfer it to other objects. In previous work, this has been implemented by
solving the dense point-to-point correspondence problem. Such an approach
requires a significant amount of supervision, often placing hundreds of
landmarks by hand. This paper proposes a functional approach for skeleton
transfer that uses limited information and does not require a complete match
between the geometries. To do so, we suggest a novel representation for the
skeleton properties, namely the functional regressor, which is compact and
invariant to different discretizations and poses. We consider our functional
regressor a new operator to adopt in intrinsic geometry pipelines for encoding
the pose information, paving the way for several new applications. We
numerically stress our method on a large set of different shapes and object
classes, providing qualitative and numerical evaluations of precision and
computational efficiency. Finally, we show a preliminar transfer of the
complete rigging scheme, introducing a promising direction for future
explorations.
