无需成对服装数据，自动生成风格匹配的穿搭
Towards Intelligent Design: A Self-driven Framework for Collocated Clothing Synthesis Leveraging Fashion Styles and Textures
- 基于风格与纹理自监督学习生成搭配衣物
- 在无成对数据下仍保持高视觉真实与搭配度
- 适合时尚生成、智能穿搭系统开发者
共置服装合成（CCS）是时尚科技中的关键课题，旨在生成与给定衣物风格协调的新服装。以往方法依赖成对服饰数据（如上衣与下装搭配）训练生成模型，需专业人员构建配对，过程繁琐耗时。本文提出自驱动框架ST-Net，通过生成对抗网络从服装的风格与纹理特征中提取搭配规则，实现无需成对数据的生成。为支持训练与评估，我们构建了大规模无监督CCS专用数据集。大量实验表明，该方法在视觉真实性与时尚搭配度上均优于现有最优基线。
原文摘要 · Abstract (English)
Collocated clothing synthesis (CCS) has emerged as a pivotal topic in fashion technology, primarily concerned with the generation of a clothing item that harmoniously matches a given item. However, previous investigations have relied on using paired outfits, such as a pair of matching upper and lower clothing, to train a generative model for achieving this task. This reliance on the expertise of fashion professionals in the construction of such paired outfits has engendered a laborious and time-intensive process. In this paper, we introduce a new self-driven framework, named style- and texture-guided generative network (ST-Net), to synthesize collocated clothing without the necessity for paired outfits, leveraging self-supervised learning. ST-Net is designed to extrapolate fashion compatibility rules from the style and texture attributes of clothing, using a generative adversarial network. To facilitate the training and evaluation of our model, we have constructed a large-scale dataset specifically tailored for unsupervised CCS. Extensive experiments substantiate that our proposed method outperforms the state-of-the-art baselines in terms of both visual authenticity and fashion compatibility.
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