ReStGAN: A step towards visually guided shopper experience via text-to-image synthesis

E-commerce companies like Amazon, Alibaba and Flip-kart have an extensive catalogue comprising of billions of products. Matching customer search queries to plausible products is challenging due to the size and diversity of the catalogue. These challenges are compounded in apparel due to the semantic complexity and a large variation of fashion styles, product attributes and colours. Providing aids that can help the customer visualise the styles and colours matching their "search queries" will provide customers with necessary intuition about what can be done next. This helps the customer buy a product with the styles, embellishments and colours of their liking. In this work, we propose a Generative Adversarial Network (GAN) for generating images from text streams like customer search queries. Our GAN learns to incrementally generate possible images complementing the fine-grained style, colour of the apparel in the query. We incorporate a novel colour modelling approach enabling the GAN to render a wide spectrum of colours accurately. We compile a dataset from an e-commerce website to train our model. The proposed approach outperforms the baselines on qualitative and quantitative evaluations.