Few-shot adversarial learning of realistic neural talking head models

Egor Zakharov, Aliaksandra Shysheya, Egor Burkov, Victor Lempitsky

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    150 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings - 2019 International Conference on Computer Vision, ICCV 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages9458-9467
    Number of pages10
    ISBN (Electronic)9781728148038
    DOIs
    Publication statusPublished - Oct 2019
    Event17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of
    Duration: 27 Oct 20192 Nov 2019

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision
    Volume2019-October
    ISSN (Print)1550-5499

    Conference

    Conference17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
    Country/TerritoryKorea, Republic of
    CitySeoul
    Period27/10/192/11/19

    Fingerprint

    Dive into the research topics of 'Few-shot adversarial learning of realistic neural talking head models'. Together they form a unique fingerprint.

    Cite this