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When creating an outfit, style is a criterion in selecting each fashion item.This means that style can be regarded as a feature of the overall outfit.However, in various previous studies on outfit generation, there have been fewmethods focusing on global information obtained from an outfit. To address thisdeficiency, we have incorporated an unsupervised style extraction module into amodel to learn outfits. Using the style information of an outfit as a whole,the proposed model succeeded in generating outfits more flexibly withoutrequiring additional information. Moreover, the style information extracted bythe proposed model is easy to interpret. The proposed model was evaluated ontwo human-generated outfit datasets. In a fashion item prediction task (missingprediction task), the proposed model outperformed a baseline method. In a styleextraction task, the proposed model extracted some easily distinguishablestyles. In an outfit generation task, the proposed model generated an outfitwhile controlling its styles. This capability allows us to generate fashionableoutfits according to various preferences.

Outfit Generation and Style Extraction via Bidirectional LSTM and Autoencoder