|
|
|
|
| LEADER |
01575 am a22002173u 4500 |
| 001 |
129718 |
| 042 |
|
|
|a dc
|
| 100 |
1 |
0 |
|a Kaspar, Alexandre
|e author
|
| 100 |
1 |
0 |
|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
|e contributor
|
| 100 |
1 |
0 |
|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
|e contributor
|
| 700 |
1 |
0 |
|a Oh, Taehyun
|e author
|
| 700 |
1 |
0 |
|a Makatura, Liane
|e author
|
| 700 |
1 |
0 |
|a Kellnhofer, Petr
|e author
|
| 700 |
1 |
0 |
|a Matusik, Wojciech
|e author
|
| 245 |
0 |
0 |
|a Neural inverse knitting: From images to manufacturing instructions
|
| 260 |
|
|
|b Proceedings of Machine Learning Research,
|c 2021-02-08T23:09:06Z.
|
| 856 |
|
|
|z Get fulltext
|u https://hdl.handle.net/1721.1/129718
|
| 520 |
|
|
|a 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.
|
| 546 |
|
|
|a en
|
| 655 |
7 |
|
|a Article
|
| 773 |
|
|
|t ICML 2019: 36th International Conference on Machine Learning
|
