Papers › Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Few-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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Talking Head Generation | VoxCeleb1 - 1-shot learning | Few-shot Adversarial Model | FID | 43.0 | #1 of 2 | Archive leaderboard | report |
| Talking Head Generation | VoxCeleb1 - 32-shot learning | Few-shot Adversarial Model | FID | 29.5 | #1 of 2 | Archive leaderboard | report |
| Talking Head Generation | VoxCeleb1 - 8-shot learning | Few-shot Adversarial Model | FID | 38.0 | #1 of 2 | Archive leaderboard | report |
| Talking Head Generation | VoxCeleb2 - 1-shot learning | Few-shot Adversarial Model | FID | 48.5 | #5 of 5 | Archive leaderboard | report |
| Talking Head Generation | VoxCeleb2 - 32-shot learning | Few-shot Adversarial Model | FID | 30.6 | #1 of 1 | Archive leaderboard | report |
| Talking Head Generation | VoxCeleb2 - 8-shot learning | Few-shot Adversarial Model | FID | 42.2 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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