Download our brand-new whitepaper, written in partnership with Frontier, to learn the secrets of machine learning-assisted material digitisation, or read the overview below:
Fashion is committed to digital product creation. Across almost every product category, and in businesses of every shape and size, critical design and development work is going digital. Workflows that used to be performed by lining up one analogue process after another – such as design visualisation, prototyping, sampling, and fitting – are being re-envisioned as joined-up digital cycles.
But despite the fashion industry’s appetite for working digitally, significant barriers still remain between the vision for digital product creation and the reality of how difficult digitisation can actually be.
Outside of truly digital-native companies, most apparel and footwear brands were not set up with digital product creation in mind. Which means that – especially in light of COVID – they have needed to adapt to a new way of working; designing a garment in 3D is a different discipline from sketching the same garment in 2D, and it requires a skillset that many brands simply did not have in-house, and have needed to either contract out or hire in.
So as the demand for digital assets has grown downstream and in the supply chain, so has the pressure on the in-house resources and service partners that brands are relying on to create digital products at scale.
This pressure is especially pronounced in material digitisation – a critical step in the digital product creation process, but one that relies heavily on specialised manual effort, costly hardware, and supply chain connectivity.
As a result, where digital product creation and 3D design originally promised rapid turnarounds, and revolutionary ways to shorten product lifecycles, four significant bottlenecks have come to characterise the material digitisation process. And as the industry’s appetite for 3D assets continues to accelerate – especially in light of growing consumer demand for virtual reality and augmented reality experiences – these bottlenecks could become even more pronounced.
New peer-reviewed academic research published by Frontier proposes tackling the problem of scaling material digitisation from a different angle: by accelerating and automating the material capture process at source, substituting intelligent, invisible machine learning for the potentially slower and more costly method of relying solely on hardware.
To make that research accessible and engaging, Frontier has partnered with The Interline to create a new whitepaper that sets out the challenges of capturing digital materials, and creating digital products, and proposes a potentially new approach.