Name
Abstract | ||
|---|---|---|
We address the problem of un-supervised geometric image-to-image translation. Rather than transferring the style of an image as a whole, our goal is to translate the geometry of an object as depicted in different domains while preserving its appearance. Towards this goal, we propose a fully un-paired model that performs shape translation within a single model and without the need of additional post-processing stages. Extensive experiments on the VITON, CMU-Multi-PIE and our own FashionStyle datasets show the effectiveness of the proposed method at achieving the task at hand. In addition, we show that despite their low-dimensionality, the features learned by our model have potential for the item retrieval task |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Journal |
Volume | Citations | PageRank |
abs/1812.02134 | 0 | 0.34 |
References | Authors | |
19 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaili Wang | 1 | 0 | 0.68 |
| Li-Qian Ma | 2 | 99 | 5.70 |
| José Oramas M. | 3 | 44 | 6.19 |
| Luc Van Gool | 4 | 27566 | 1819.51 |
| Tinne Tuytelaars | 5 | 10161 | 609.66 |