Our good friend Froggy tagged a few of us in a video on Twitter which took me to this;
Can you chippers with brains have a look and see what we have here please? The video related this to Samsung AI research centre.
https://arxiv.org/abs/1905.08233v1Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Egor Zakharov, Aliaksandra Shysheya, Egor Burkov, Victor Lempitsky
Several recent works have shown how highly realistic human head images can be obtained by training convolutional neural networks to generate them. In order to create a personalized talking head model, these works require training on a large dataset of images of a single person. However, in many practical scenarios, such personalized talking head models need to be learned from a few image views of a person, potentially even a single image. Here, we present a system with such few-shot capability. It performs lengthy meta-learning on a large dataset of videos, and after that is able to frame few- and one-shot learning of neural talking head models of previously unseen people as adversarial training problems with high capacity generators and discriminators. Crucially, the system is able to initialize the parameters of both the generator and the discriminator in a person-specific way, so that training can be based on just a few images and done quickly, despite the need to tune tens of millions of parameters. We show that such an approach is able to learn highly realistic and personalized talking head models of new people and even portrait paintings.Several recent works have shown how highly realistic human head images can be obtained by training convolutional neural networks to generate them. In order to create a personalized talking head model, these works require training on a large dataset of images of a single person. However, in many practical scenarios, such personalized talking head models need to be learned from a few image views of a person, potentially even a single image. Here, we present a system with such few-shot capability. It performs lengthy meta-learning on a large dataset of videos, and after that is able to frame few- and one-shot learning of neural talking head models of previously unseen people as adversarial training problems with high capacity generators and discriminators. Crucially, the system is able to initialize the parameters of both the generator and the discriminator in a person-specific way, so that training can be based on just a few images and done quickly, despite the need to tune tens of millions of parameters. We show that such an approach is able to learn highly realistic and personalized talking head models of new people and even portrait paintings.
- Forums
- ASX - By Stock
- BRN
- 2020 BRN Discussion
2020 BRN Discussion, page-27319
-
-
- There are more pages in this discussion • 1,995 more messages in this thread...
You’re viewing a single post only. To view the entire thread just sign in or Join Now (FREE)
Featured News
Add BRN (ASX) to my watchlist
(20min delay)
|
|||||
Last
17.5¢ |
Change
0.005(2.94%) |
Mkt cap ! $343.4M |
Open | High | Low | Value | Volume |
16.5¢ | 18.5¢ | 16.5¢ | $1.865M | 10.58M |
Buyers (Bids)
No. | Vol. | Price($) |
---|---|---|
36 | 1001708 | 17.0¢ |
Sellers (Offers)
Price($) | Vol. | No. |
---|---|---|
17.5¢ | 167790 | 3 |
View Market Depth
No. | Vol. | Price($) |
---|---|---|
31 | 773061 | 0.170 |
26 | 568227 | 0.165 |
27 | 1452882 | 0.160 |
47 | 1519084 | 0.155 |
117 | 3652569 | 0.150 |
Price($) | Vol. | No. |
---|---|---|
0.175 | 158299 | 2 |
0.180 | 698170 | 7 |
0.185 | 1095940 | 16 |
0.190 | 757076 | 18 |
0.195 | 744472 | 12 |
Last trade - 16.10pm 13/09/2024 (20 minute delay) ? |
Featured News
BRN (ASX) Chart |
The Watchlist
LPM
LITHIUM PLUS MINERALS LTD.
Simon Kidston, Non--Executive Director
Simon Kidston
Non--Executive Director
SPONSORED BY The Market Online