  Hand motion capture data is now relatively easy to obtain, even for
complicated grasps; however this data is of limited use without the ability to
retarget it onto the hands of a specific character or robot. The target hand
may differ dramatically in geometry, number of degrees of freedom (DOFs), or
number of fingers. We present a simple, but effective framework capable of
kinematically retargeting multiple human hand-object manipulations from a
publicly available dataset to a wide assortment of kinematically and
morphologically diverse target hands through the exploitation of contact areas.
We do so by formulating the retarget operation as a non-isometric shape
matching problem and use a combination of both surface contact and marker data
to progressively estimate, refine, and fit the final target hand trajectory
using inverse kinematics (IK). Foundational to our framework is the
introduction of a novel shape matching process, which we show enables
predictable and robust transfer of contact data over full manipulations while
providing an intuitive means for artists to specify correspondences with
relatively few inputs. We validate our framework through thirty demonstrations
across five different hand shapes and six motions of different objects. We
additionally compare our method against existing hand retargeting approaches.
Finally, we demonstrate our method enabling novel capabilities such as object
substitution and the ability to visualize the impact of design choices over
full trajectories.
