GIF: Generative Interpretable Faces

Partha Ghosh, Pravir Singh Gupta, Roy Uziel, Anurag Ranjan, Michael J. Black, Timo Bolkart

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

7 Scopus citations

Abstract

Photo-realistic visualization and animation of expressive human faces have been a long standing challenge. 3D face modeling methods provide parametric control but generates unrealistic images, on the other hand, generative 2D models like GANs (Generative Adversarial Networks) output photo-realistic face images, but lack explicit control. Recent methods gain partial control, either by attempting to disentangle different factors in an unsupervised manner, or by adding control post hoc to a pre-trained model. Unconditional GANs, however, may entangle factors that are hard to undo later. We condition our generative model on pre-defined control parameters to encourage disentanglement in the generation process. Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model. While conditioning on FLAME parameters yields unsatisfactory results, we find that conditioning on rendered FLAME geometry and photometric details works well. This gives us a generative 2D face model named GIF (Generative Interpretable Faces) that offers FLAME's parametric control. Here, interpretable refers to the semantic meaning of different parameters. Given FLAME parameters for shape, pose, expressions, parameters for appearance, lighting, and an additional style vector, GIF outputs photo-realistic face images. We perform an AMT based perceptual study to quantitatively and qualitatively evaluate how well GIF follows its conditioning. The code, data, and trained model are publicly available for research purposes at http://gif.is.tue.mpg.de.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on 3D Vision, 3DV 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages868-878
Number of pages11
ISBN (Electronic)9781728181288
DOIs
StatePublished - 1 Nov 2020
Event8th International Conference on 3D Vision, 3DV 2020 - Virtual, Fukuoka, Japan
Duration: 25 Nov 202028 Nov 2020

Publication series

NameProceedings - 2020 International Conference on 3D Vision, 3DV 2020

Conference

Conference8th International Conference on 3D Vision, 3DV 2020
Country/TerritoryJapan
CityVirtual, Fukuoka
Period25/11/2028/11/20

Keywords

  • Conditional GANs
  • Disentanglement
  • FaceAnimation
  • GANs
  • Generative models
  • PhotorealisticImageGeneration

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

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