Name
Abstract | ||
|---|---|---|
We present ObamaNet, the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text. Contrary to other published lip-sync approaches, ours is only composed of fully trainable neural modules and does not rely on any traditional computer graphics methods. More precisely, we use three main modules: a text-to-speech network based on Char2Wav, a time-delayed LSTM to generate mouth-keypoints synced to the audio, and a network based on Pix2Pix to generate the video frames conditioned on the keypoints. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Computer vision,Architecture,Pattern recognition,Computer science,Artificial intelligence,Computer graphics,Lip sync |
DocType | Volume | Citations |
Journal | abs/1801.01442 | 3 |
PageRank | References | Authors |
0.39 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rithesh Kumar | 1 | 8 | 1.81 |
| Jose Sotelo | 2 | 9 | 2.16 |
| Kundan Kumar | 3 | 10 | 5.89 |
| Alexandre de Brébisson | 4 | 5 | 0.76 |
| Yoshua Bengio | 5 | 42677 | 3039.83 |