# Neural Inverse Knitting: From Images to Manufacturing Instructions

arXiv: https://arxiv.org/abs/1902.02752 · HTML: https://mlsanity.com/paper/1902.02752/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [Neural inverse procedural modeling of knitting yarns from images](https://mlsanity.com/paper/2303.00154/) (2023)
- [CrochetBench: Can Vision-Language Models Move from Describing to Doing in Crochet Domain?](https://mlsanity.com/paper/2511.09483/) (2025)
- [Using Artificial Intelligence for the Automation of Knitting Patterns](https://mlsanity.com/paper/2309.11202/) (2023)
- [Knitting Robots: A Deep Learning Approach for Reverse-Engineering Fabric Patterns](https://mlsanity.com/paper/2504.14007/) (2025)

## Abstract (arXiv)

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.