Predicting Next-Season Designs on High Fashion Runway

TL;DR

This paper introduces a neural network-based framework that leverages runway show data to predict next-season fashion designs, achieving high accuracy in forecasting designer styles and trends.

Contribution

It presents a novel approach combining runway embedding learning and RNN/LSTM models to predict future fashion designs based on historical runway data.

Findings

Achieves 78.42% average AUC in predictions

Reaches 95% accuracy for individual designers

Demonstrates effective modeling of fashion trend evolution

Abstract

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.