Efficient clothing fitting from data

Abstract

A major drawback of shopping for clothes on-line is that the customer cannot try on clothes and see if they fit or suit them. One solution is to display clothing on an avatar, a 3D graphical model of the customer. However the normal technique for modeling clothing in computer graphics, cloth dynamics, suffers from being too processor intensive and is not practical for real time applications. Hence, retailers normally rely on a fixed set of body models to which clothes are pre-fitted. As the customer has to choose from this limited set the fit is typicallly not very representative of how the real clothes will fit. We propose a method that uses a compromise between these two methods. We generate a set of example avatars by performing Principal Component Analysis on a dataset of avatars. Clothes are pre-fitted to these examples off-line. Instead of asking the customer to choose from the set of examples we are able to represent the users avatar as a weighted sum of the examples, we then fit clothes as the same weighted sum over the clothes fitted to the examples.