Personalizing Apparel using Neural Style Transfer

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Date_umbc_0434M_11672.pdf (2.78 MB)

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http://hdl.handle.net/11603/15500

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UMBC Theses and Dissertations

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Author/Creator ORCID

Date

2017-01-01

Type of Work

theses
Text

Department

Computer Science and Electrical Engineering

Program

Computer Science

Citation of Original Publication

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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Distribution Rights granted to UMBC by the author.

Subjects

Convolutional Neural Networks
Fashion
Neural Networks
Personalization
Style Transfer
Texture Synthesis

Abstract

Convolutional Neural Networks have been highly successful in performing a set of computer vision tasks such as object recognition, object detection, image segmentation and texture syntheses. Gatys et al. (Gatys, Ecker, & Bethge 2015b) show how the artistic 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 theses, the neural style transfer method is applied to fashion to synthesize custom clothes. We create a personalization model that is able to generate new custom clothes based on a user's preference and by learning the user's fashion choices from a limited set of clothes from their closet. The approach is evaluated by analyzing the generated images of clothes and how they align with the user's fashion style.