  Commonly used linear and nonlinear constitutive material models in
deformation simulation contain many simplifications and only cover a tiny part
of possible material behavior. In this work we propose a framework for learning
customized models of deformable materials from example surface trajectories.
The key idea is to iteratively improve a correction to a nominal model of the
elastic and damping properties of the object, which allows new forward
simulations with the learned correction to more accurately predict the behavior
of a given soft object. Space-time optimization is employed to identify gentle
control forces with which we extract necessary data for model inference and to
finally encapsulate the material correction into a compact parametric form.
Furthermore, a patch based position constraint is proposed to tackle the
challenge of handling incomplete and noisy observations arising in real-world
examples. We demonstrate the effectiveness of our method with a set of
synthetic examples, as well with data captured from real world homogeneous
elastic objects.
