Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have come up with an approach to streamline the knitting process: a new system and design tool for automating knitted garments.
In one paper, a team of researchers created a system called ‘InverseKnit’ that converts photos of knitted patterns into instructions that are then used with machines to make clothing.
Even the casual users could create designs without a memory bank of coding knowledge, and most importantly, eliminate the issues of less efficiency and waste in manufacturing, MIT researchers explained.
“As far as machines and knitting go, this type of system could change accessibility for people looking to be the designers of their own items,” says Alexandre Kaspar, CSAIL Ph.D. student.
“We want to let casual users get access to machines without needed programming expertise, so they can reap the benefits of customization by making use of machine learning for design and manufacturing,” Alexandre added.
To get InverseKnit active and running, the team first created a dataset of knitting instructions, and the matching images of those patterns. Then trained their deep neural network on that data to interpret the 2-D knitting instructions from images.
When testing InverseKnit, the team found that it produced accurate instructions 94% percent of the time.
“Current state-of-the-art computer vision techniques are data-hungry, and they need many examples to model the world effectively,” says Jim McCann, assistant professor in the Carnegie Mellon Robotics Institute.
Jim McCann further added, “With InverseKnit, the team collected an immense dataset of knit samples that, for the first time, enables modern computer vision techniques to be used to recognize and parse knitting patterns.”
The team tested the usability of CADKnit by having non-expert users create patterns for their garments and adjust the size and shape. In post-test surveys, the users said they found it easy to manipulate and customize their socks or beanies, successfully fabricating multiple knitted samples.
They noted that lace patterns were tricky to design correctly and would benefit from fast realistic simulation.
However, the system is only the first step towards full garment customization. The authors found that garments with complicated interfaces between different parts – such as sweaters — didn’t work well with the design tool.
The trunk of sweaters and sleeves can be connected in various ways, and the software didn’t yet have a way of describing the whole design space for that.
Furthermore, the current system can only use one yarn for a shape, but the team hopes to improve this by introducing a stack of yarn at each stitch. To enable work with more complex patterns and larger shapes, the researchers plan to use hierarchical data structures that don’t incorporate all stitches, just the necessary ones.