The fashion industry did $3 trillion in business, 2% of global GDP in 2018; e-commerce fashion amounted to $520 billion in 2019. AI is poised to revolutionize the fashion industry by providing insights into fashion trends, purchase patterns, and enabling better inventory management. The global brand H&M has been applying AI solutions to boost business operations. One example is a system to organize and allocate masses of unsold stock to retail stories with highest demand, reducing the need for discounted sales. This is achieved by optimizing the supply chain and inventory management, reducing the amount of wasted clothing.
In January, what had previously only been pixels made a real-world splash on the catwalks of Paris Fashion Week. The models' futuristic-looking clothes, designed in a collaboration between fashion house Acne Studios and artist and programmer Robbie Barrat, were designed by an artificial intelligence (AI). 'When you design a collection, you have an idea of what a jacket looks like, or a pair of trousers,' says Jonny Johansson, creative director of Acne Studios. 'The computer doesn't know what a jacket is. It tries to learn from the images we gave it, and then creates its own idea.
The T-shirts sold by Cross & Freckle, a New York–based fashion upstart, don't look revolutionary at first glance. They come in black or white, they're cut for a unisex fit, and they sell for $25. Each of them has a little design embroidered into the cotton that references staples of New York City life: pigeons, dollar pizza slices, subway rats. They were designed instead by a neural network, which crunched doodle data from millions of people and spit out the original art that makes up the embroidery. Cross & Freckle isn't the first company to use AI to generate art--people have been doing that for years.
This Friday, Artificial Intelligence fashion startup Bigthinx, in partnership with Fashinnovation, will live stream the first fully digital 3D Virtual Fashion Show (including digitised human models) since the coronavirus pandemic forced the fashion industry online. The'virtual' aspect is that the models and clothes are being created using 3D digital design, rendering, and animation, based on technical data (including garment measurements) and photographs of the models and clothes. This will be the first time many fashion professionals have seen virtual fashion since the industry-wide discussions about implementing it ramped up, following the coronavirus-induced lockdown. In creating this 3D virtual show, with opportunity comes numerous challenges, especially for a technology company known for its'body scan' avatar solution based on just two photos and a selfie from a smartphone. From these images, they calculate "44 precise body measurements and body composition ratios, with over 95% accuracy."
Fashion is a fast-changing industry where designs are refreshed at large scale every season. Moreover, it faces huge challenge of unsold inventory as not all designs appeal to customers. This puts designers under significant pressure. Firstly, they need to create innumerous fresh designs. Secondly, they need to create designs that appeal to customers. Although we see advancements in approaches to help designers analyzing consumers, often such insights are too many. Creating all possible designs with those insights is time consuming. In this paper, we propose a system of AI assistants that assists designers in their design journey. The proposed system assists designers in analyzing different selling/trending attributes of apparels. We propose two design generation assistants namely Apparel-Style-Merge and Apparel-Style-Transfer. Apparel-Style-Merge generates new designs by combining high level components of apparels whereas Apparel-Style-Transfer generates multiple customization of apparels by applying different styles, colors and patterns. We compose a new dataset, named DeepAttributeStyle, with fine-grained annotation of landmarks of different apparel components such as neck, sleeve etc. The proposed system is evaluated on a user group consisting of people with and without design background. Our evaluation result demonstrates that our approach generates high quality designs that can be easily used in fabrication. Moreover, the suggested designs aid to the designers creativity.