Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Open accessAbstract
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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Recent works on adversarial perturbation attacks to fool DNN-based detectors make the deepfake detection task more difficult.
“Recent works on adversarial perturbation attacks to fool DNN-based detectors make the deepfake detection task more difficult [157, 171–174].”
From Deep learning for deepfakes creation and detection: A survey (2022) · cites this paper as [172]
The Introduction discusses adversarially trained networks for generating realistic talking heads, and §3.2 describes a discriminator used during generator training. Neither passage addresses adversarial perturbation attacks against DNN-based deepfake detectors or their effect on detection difficulty (searched for those terms and paraphrases). (LLM, conf=0.76)
Experimental — accuracy not yet measuredZakharov et al. used the cosine similarity between embedding vectors of the ArcFace network for measuring identity mismatch.
“Zakharov et al . [117] used the cosine similarity between embedding vectors of the ArcFace network [118] for measuring identity mismatch.”
From Deep learning for visual speech analysis: A survey (2024) · cites this paper as [117]
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Method
- Authors
- Zakharov, E., Shysheya, A., Burkov, E., & Lempitsky, V.
- Affiliations
- Skolkovo Institute of Science and Technology (RU)
- Samsung (South Korea) (KR)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 9458-9467
- OpenAlex
- W2945052639
- Cited by
- 687
- References
- 46
- Citations by year
- 2026: 19 · 2025: 68 · 2024: 86 · 2023: 123 · 2022: 130 · 2021: 135 · 2020: 104 · 2019: 22
- FWCI
- 31.89
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- few-shot learning, one-shot learning, adversarial training, meta-learning, generative adversarial networks
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