Name
Abstract | ||
|---|---|---|
This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement. In this problem there exist two major technical challenges: 1) designing a robust translation model between static images and dynamic videos, and 2) preserving facial identity during image-video translation. To address such two problems, we generalize the state-of-the-art image-to-image translation network (Cycle-Consistent Adversarial Networks) to the image-to-video/video-to-image translation context by exploiting a image-video translation model and an identity preservation model. In particular, we apply the state-of-the-art Wasserstein GAN technique to the setting of image-video translation for better convergence, and we meanwhile introduce a face verificator to ensure the identity. Experiments on standard image/video face datasets demonstrate the effectiveness of the proposed model in both terms of qualitative and quantitative evaluations. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Convergence (routing),Quantitative Evaluations,Computer science,Artificial intelligence,Identity preservation,Machine learning,Adversarial system |
DocType | Volume | Citations |
Journal | abs/1712.00971 | 1 |
PageRank | References | Authors |
0.35 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiwu Huang | 1 | 252 | 15.26 |
| Bernhard Kratzwald | 2 | 26 | 5.47 |
| Danda Pani Paudel | 3 | 3 | 1.39 |
| Jiqing Wu | 4 | 28 | 3.14 |
| Luc Van Gool | 5 | 27566 | 1819.51 |