---
title: "Example: Cloth Fitting Prediction"
source: https://www.jemoka.com/posts/kbhexample_cloth_fitting_prediction/
---

Have: \(m\) training data points \((\theta_{i}, \phi_{i})\) generated from the true/approximated function \(\phi_{i} = f\qty (\theta_{i})\) (which uses physical simulation/CV techniques). Training data here is *very expensive and lots of errors
Want: \(\hat{f}\qty(\theta) = f\qty(\theta)\)
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&mdash;there are highly non-linear rotations! which is not really easy to make with standard model functions using \(\hat{f}\).
