Neural inverse knitting: From images to manufacturing instructions
Name
1902.02752.pdf
Description
Accepted version
Size
6.37 MB
Format
Adobe PDF
Checksum (MD5)
561a74fc4ecef597db8cde3fc84a50e7
Author(s)
Kaspar, Alexandre
Oh, Taehyun
Makatura, Liane
Kellnhofer, Petr
Matusik, Wojciech
Date Issued
2019
Journal
ICML 2019: 36th International Conference on Machine Learning
Publisher
Proceedings of Machine Learning Research
Citation
Kaspar, Alexandre et al. "Neural inverse knitting: From images to manufacturing instructions." ICML 2019: 36th International Conference on Machine Learning, June, 2019, Long Beach, California, Proceedings of Machine Learning Research, 2019. © 2019 The Authors
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://proceedings.mlr.press/v97/kaspar19a.html
