  When considering sparse motion capture marker data, one typically struggles
to balance its overfitting via a high dimensional blendshape system versus
underfitting caused by smoothness constraints. With the current trend towards
using more and more data, our aim is not to fit the motion capture markers with
a parameterized (blendshape) model or to smoothly interpolate a surface through
the marker positions, but rather to find an instance in the high resolution
dataset that contains local geometry to fit each marker. Just as is true for
typical machine learning applications, this approach benefits from a plethora
of data, and thus we also consider augmenting the dataset via specially
designed physical simulations that target the high resolution dataset such that
the simulation output lies on the same so-called manifold as the data targeted.

Computer Vision and Pattern Recognition