Have: m training data points (\theta_{i}, \phi_{i}) generated from the true/approximated function \phi_{i} = f\left(\theta_{i}\right) (which uses physical simulation/CV techniques). Training data here is *very expensive and lots of errors Want: \hat{f}\left(\theta\right) = f\left(\theta\right) Problem: as joints rotate (which is highly nonlinear), cloth verticies move in complex and non-linear ways which are difficult to handle with a standard neural network—there are highly non-linear rotations! which is not really easy to make with standard model functions using \hat{f}.