Personalizing Apparel using Neural Style Transfer
Date
2017-01-01Type of Work
Textthesis
Department
Computer Science and Electrical EngineeringProgram
Computer ScienceRights
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.eduDistribution Rights granted to UMBC by the author.
Subjects
Convolutional Neural NetworksFashion
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 synthesis. 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 thesis, 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.