Name
Abstract | ||
|---|---|---|
Fine-grained facial expression aims at changing the expression of an image without altering facial identity. Most current expression manipulation methods are based on a discrete expression label, which mainly manipulates holistic expression with details neglected. To handle the above mentioned problems, we propose an end-to-end expression-guided generative adversarial network (EGGAN), which synthe... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MMUL.2021.3061544 | IEEE MultiMedia |
Keywords | DocType | Volume |
Gold,Generative adversarial networks,Training,Gallium nitride,Faces,Semantics,Facial features | Journal | 28 |
Issue | ISSN | Citations |
3 | 1070-986X | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junshu Tang | 1 | 0 | 0.34 |
| Zhiwen Shao | 2 | 10 | 4.23 |
| Lizhuang Ma | 3 | 498 | 100.70 |