A Generative 3D Facial Model by Adversarial Training - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2019

A Generative 3D Facial Model by Adversarial Training

Résumé

We consider data-driven generative models for the 3D face, and focus in particular on factorized representations that can decouple sources of variation, typically identity and expression with faces. Such models provide semantically meaningful parameterizations, but existing methods are still limited in their ability to effectively learn the variability with respect to natural factors, especially when only sparse label information is available. In this work we explore a new direction for this problem by using Generative Adversarial Networks. We build in particular on auxiliary classifier GANs to design a model that maps 3D face shapes into a latent space where identity and expression attributes are explicitly disentangled. Our experiments demonstrate that GANs can contribute with better decoupling performances while achieving competitive model accuracy.
Fichier principal
Vignette du fichier
3d-face-gan.pdf (4.98 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02064711 , version 1 (12-03-2019)
hal-02064711 , version 2 (18-04-2019)
hal-02064711 , version 3 (07-09-2019)

Identifiants

  • HAL Id : hal-02064711 , version 1

Citer

Victoria Fernández Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, Edmond Boyer. A Generative 3D Facial Model by Adversarial Training. 2019. ⟨hal-02064711v1⟩
668 Consultations
1194 Téléchargements

Partager

Gmail Facebook X LinkedIn More