Fashioning with Networks: Neural Style Transfer to Design Clothes

TL;DR

This paper applies neural style transfer to fashion, enabling personalized clothing design by learning individual preferences from limited wardrobe data and synthesizing new custom garments.

Contribution

It introduces a novel approach to personalize fashion design using neural style transfer, tailored to individual user preferences and limited clothing data.

Findings

Generated clothes align with user style preferences

Method successfully personalizes fashion synthesis

Effective with limited user data

Abstract

Convolutional Neural Networks have been highly successful in performing a host of computer vision tasks such as object recognition, object detection, image segmentation and texture synthesis. In 2015, Gatys et. al [7] show how the style of a painter can be extracted from an image of the painting and applied to another normal photograph, thus recreating the photo in the style of the painter. The method has been successfully applied to a wide range of images and has since spawned multiple applications and mobile apps. In this paper, the neural style transfer algorithm is applied to fashion so as to synthesize new custom clothes. We construct an approach to personalize and generate new custom clothes based on a users preference and by learning the users fashion choices from a limited set of clothes from their closet. The approach is evaluated by analyzing the generated images of clothes and how well they align with the users fashion style.