skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

GANimation: Anatomically-Aware Facial Animation from a Single Image

Computer Vision – ECCV 2018, 2018-01, p.835-851 [Peer Reviewed Journal]

Springer Nature Switzerland AG 2018 ;info:eu-repo/semantics/openAccess ;ISSN: 0302-9743 ;ISBN: 3030012484 ;ISBN: 9783030012489 ;EISSN: 1611-3349 ;EISBN: 3030012492 ;EISBN: 9783030012496 ;DOI: 10.1007/978-3-030-01249-6_50

Digital Resources/Online E-Resources

Citations Cited by
  • Title:
    GANimation: Anatomically-Aware Facial Animation from a Single Image
  • Author: Pumarola, Albert ; Agudo, Antonio ; Martinez, Aleix M. ; Sanfeliu, Alberto ; Moreno-Noguer, Francesc
  • Subjects: Action-Unit Condition ; Automàtica i control ; Classificació INSPEC ; computer vision ; Face Animation ; GANs ; Informàtica ; Pattern recognition ; Àrees temàtiques de la UPC
  • Is Part Of: Computer Vision – ECCV 2018, 2018-01, p.835-851
  • Description: Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis. The most successful architecture is StarGAN, that conditions GANs’ generation process with images of a specific domain, namely a set of images of persons sharing the same expression. While effective, this approach can only generate a discrete number of expressions, determined by the content of the dataset. To address this limitation, in this paper, we introduce a novel GAN conditioning scheme based on Action Units (AU) annotations, which describes in a continuous manifold the anatomical facial movements defining a human expression. Our approach allows controlling the magnitude of activation of each AU and combine several of them. Additionally, we propose a fully unsupervised strategy to train the model, that only requires images annotated with their activated AUs, and exploit attention mechanisms that make our network robust to changing backgrounds and lighting conditions. Extensive evaluation show that our approach goes beyond competing conditional generators both in the capability to synthesize a much wider range of expressions ruled by anatomically feasible muscle movements, as in the capacity of dealing with images in the wild.
  • Publisher: Cham: Springer International Publishing
  • Language: English
  • Identifier: ISSN: 0302-9743
    ISBN: 3030012484
    ISBN: 9783030012489
    EISSN: 1611-3349
    EISBN: 3030012492
    EISBN: 9783030012496
    DOI: 10.1007/978-3-030-01249-6_50
  • Source: Recercat
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait