用轮廓图和风格指导，生成搭配协调的服装图像。
COutfitGAN: Learning to Synthesize Compatible Outfits Supervised by Silhouette Masks and Fashion Styles
- 基于轮廓掩码与风格特征，生成互补服装。
- 在20万+穿搭数据上训练，生成效果更真实、搭配更合理。
- 适合时尚设计、虚拟试衣等应用，可辅助创意搭配。
如何推荐穿搭近年来在学术界和工业界受到广泛关注。尽管已有大量研究致力于学习服装搭配兼容性，以判断一组服装是否协调，但这些方法多聚焦于评估现有穿搭，很少将此类知识用于“设计”新服装。本文提出新任务：根据任意数量给定服装，生成与其他服装互补且协调的新服装图像。具体而言，给定一组可组成穿搭的服装，目标是合成其他互补服装的逼真图像，使其与原服装协调。为此，我们提出名为COutfitGAN的穿搭生成框架，包含金字塔风格提取器、穿搭生成器、基于UNet的真假判别器以及搭配判别器。为训练与评估该框架，我们从互联网收集了一个大规模时尚穿搭数据集，包含超过20万套穿搭和80万件服装单品。大量实验表明，COutfitGAN在相似性、真实性及搭配性指标上均优于其他基线方法。
原文摘要 · Abstract (English)
How to recommend outfits has gained considerable attention in both academia and industry in recent years. Many studies have been carried out regarding fashion compatibility learning, to determine whether the fashion items in an outfit are compatible or not. These methods mainly focus on evaluating the compatibility of existing outfits and rarely consider applying such knowledge to 'design' new fashion items. We propose the new task of generating complementary and compatible fashion items based on an arbitrary number of given fashion items. In particular, given some fashion items that can make up an outfit, the aim of this paper is to synthesize photo-realistic images of other, complementary, fashion items that are compatible with the given ones. To achieve this, we propose an outfit generation framework, referred to as COutfitGAN, which includes a pyramid style extractor, an outfit generator, a UNet-based real/fake discriminator, and a collocation discriminator. To train and evaluate this framework, we collected a large-scale fashion outfit dataset with over 200K outfits and 800K fashion items from the Internet. Extensive experiments show that COutfitGAN outperforms other baselines in terms of similarity, authenticity, and compatibility measurements.
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