Predicting Next-Season Designs on High Fashion Runway

Fashion is a large and fast-changing industry. Foreseeing the upcoming fashion trends is beneficial for fashion designers, consumers, and retailers. However, fashion trends are often perceived as unpredictable due to the enormous amount of factors involved into designers' subjectivity. In this paper, we propose a fashion trend prediction framework and design neural network models to leverage structured fashion runway show data, learn the fashion collection embedding, and further train RNN/LSTM models to capture the designers' style evolution. Our proposed framework consists of (1) a runway embedding learning model that uses fashion runway images to learn every season's collection embedding, and (2) a next-season fashion design prediction model that leverage the concept of designer style and trend to predict next-season design given designers. Through experiments on a collected dataset across 32 years of fashion shows, our framework can achieve the best performance of 78.42% AUC on average and 95% for an individual designer when predicting the next season's design.