Our team aims to resolve the key hurdles for apparel e-shoppers, most notably body and garment size detection, fit recommendation, and virtual try-on.
Using mobile 3D technology, computer vision and machine learning, as well as numerical optimisation, we strive to dramatically reduce fashion e-commerce return rates.
US returns alone create 5 billion pounds of landfill waste and 15 million tonnes of carbon emissions annually, equivalent to the amount of trash produced by 5 million people in a year, according to one estimate.
Lugano, 2017: the original founders of MOSTFIT set out to solve the fashion size, fit, and look challenge by applying self-learning algorithms to 3D human point clouds to accurately measure body and apparel dimensions.
With the arrival of ‘world-facing’ depth cameras on smartphones and thanks to a successful Innosuisse grant, the extended team is now committed to make MOSTFIT available on mobile platforms.
This requires not only the development of new algorithms, but also novel user interactions for this emerging sensor technology.
The inventors of the initial MOSTFIT technology have a background in technical fashion development and computer science.
The recent team extension now includes the Institute for Data Science and the Institute for Interactive Technologies of FHNW in Brugg.
Commercial and other implementation related support is provided by CP START-UP and a number of Swiss fashion companies as displayed herein underneath.
By helping e-shoppers find the right size and fit at once, we aim to decrease apparel e-commerce returns by up to 25%, thus contributing to three critical benefits for society and environment:
Fachhochschule Nordwestschweiz FHNW - Gebäude Nord, Steinackerstr. 5, Windisch, Aargau 5210, Switzerland