Name
Abstract | ||
|---|---|---|
We address the problem of unpaired 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 while preserving its appearance. Our model is trained without the need for paired images. It performs all steps of the shape transfer within a single model and without additional post-processing stages. Experiments on clothing-based datasets show the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICIP40778.2020.9190940 | 2020 IEEE International Conference on Image Processing (ICIP) |
Keywords | DocType | ISSN |
Shape,Clothing,Task analysis,Streaming media,Geometry,Gallium nitride,Decoding | Conference | 1522-4880 |
ISBN | Citations | PageRank |
978-1-7281-6395-6 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaili Wang | 1 | 0 | 0.68 |
| Liqian Ma | 2 | 134 | 6.90 |
| Jose Oramas M | 3 | 0 | 0.34 |
| Luc Van Gool | 4 | 0 | 0.34 |
| Tinne Tuytelaars | 5 | 10161 | 609.66 |