Teaching DNNs to design fast fashion

TL;DR

This paper introduces an automated system that analyzes social media trends to generate fashion designs rapidly, enabling fast response to trends and reducing waste in fashion production.

Contribution

It presents a novel system that detects, synthesizes, and visualizes fashion trends from social media data to assist designers in creating rapid, trend-responsive apparel.

Findings

System effectively detects trending styles from social media.

Generates diverse fashion prototypes based on detected trends.

Facilitates rapid design iteration and visualization for designers.

Abstract

$ $"Fast Fashion" spearheads the biggest disruption in fashion that enabled to engineer resilient supply chains to quickly respond to changing fashion trends. The conventional design process in commercial manufacturing is often fed through "trends" or prevailing modes of dressing around the world that indicate sudden interest in a new form of expression, cyclic patterns, and popular modes of expression for a given time frame. In this work, we propose a fully automated system to explore, detect, and finally synthesize trends in fashion into design elements by designing representative prototypes of apparel given time series signals generated from social media feeds. Our system is envisioned to be the first step in design of Fast Fashion where the production cycle for clothes from design inception to manufacturing is meant to be rapid and responsive to current "trends". It also works to reduce wastage in fashion production by taking in customer feedback on sellability at the time of design generation. We also provide an interface wherein the designers can play with multiple trending styles in fashion and visualize designs as interpolations of elements of these styles. We aim to aid the creative process through generating interesting and inspiring combinations for a designer to mull by running them through her key customers.