  We present a virtual reality (VR) framework to automate the data collection
process in cloth folding tasks. The framework uses skeleton representations to
help the user define the folding plans for different classes of garments,
allowing for replicating the folding on unseen items of the same class. We
evaluate the framework in the context of automating garment folding tasks. A
quantitative analysis is performed on 3 classes of garments, demonstrating that
the framework reduces the need for intervention by the user. We also compare
skeleton representations with RGB and binary images in a classification task on
a large dataset of clothing items, motivating the use of the framework for
other classes of garments.
