Neural Inverse Knitting: From Images to Manufacturing Instructions

Abstract

Motivated by the recent potential of mass customization brought by whole-garment knitting machines, we introduce the new problem of automatic machine instruction generation using a single image of the desired physical product, which we apply to machine knitting. We propose to tackle this problem by directly learning to synthesize regular machine instructions from real images. We create a cured dataset of real samples with their instruction counterpart and propose to use synthetic images to augment it in a novel way. We theoretically motivate our data mixing framework and show empirical results suggesting that making real images look more synthetic is beneficial in our problem setup.

arXiv commentProject page: http://deepknitting.csail.mit.edu

References in corpus (7)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Instance Normalization: The Missing Ingredient for Fast Stylization
• Conditional Random Fields as Recurrent Neural Networks
• Domain Adaptation: Learning Bounds and Algorithms
• RobustFill: Neural Program Learning under Noisy I/O
• Recent Advances in Neural Program Synthesis
• A Theory of Output-Side Unsupervised Domain Adaptation

Cited by in corpus (2)

• Knitting Skeletons: A Computer-Aided Design Tool for Shaping and Patterning of Knitted Garments
• Knitting Robots: A Deep Learning Approach for Reverse-Engineering Fabric Patterns