Name
Abstract | ||
|---|---|---|
Generative adversarial networks achieve great performance in photorealistic image synthesis in various domains, including human images. However, they usually employ latent vectors that encode the sampled outputs globally. This does not allow convenient control of semantically relevant individual parts of the image, and cannot draw samples that only differ in partial aspects, such as clothing style... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/3DV53792.2021.00036 | 2021 International Conference on 3D Vision (3DV) |
Keywords | DocType | ISSN |
n/a | Conference | 2378-3826 |
ISBN | Citations | PageRank |
978-1-6654-2688-6 | 2 | 0.39 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| kripasindhu sarkar | 1 | 19 | 5.45 |
| Lingjie Liu | 2 | 2 | 0.39 |
| Vladislav Golyanik | 3 | 22 | 12.55 |
| Christian Theobalt | 4 | 2 | 0.39 |