Fashion-AttGAN: Attribute-Aware Fashion Editing with Multi-Objective GAN
Qing Ping, Bing Wu, Wanying Ding, Jiangbo Yuan
TL;DR
This paper presents Fashion-AttGAN, a new multi-objective GAN model for attribute-aware fashion editing, along with a new dataset, demonstrating improved editing capabilities over previous models.
Contribution
The paper introduces Fashion-AttGAN, a novel model for attribute-aware fashion editing, and provides a new dataset with 14,221 images and 22 attributes for this task.
Findings
Fashion-AttGAN outperforms AttGAN in fashion editing tasks.
A new dataset with 14,221 images and 22 attributes is introduced.
Experimental results confirm the effectiveness of Fashion-AttGAN.
Abstract
In this paper, we introduce attribute-aware fashion-editing, a novel task, to the fashion domain. We re-define the overall objectives in AttGAN and propose the Fashion-AttGAN model for this new task. A dataset is constructed for this task with 14,221 and 22 attributes, which has been made publically available. Experimental results show the effectiveness of our Fashion-AttGAN on fashion editing over the original AttGAN.
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Handwritten Text Recognition Techniques · 3D Shape Modeling and Analysis