Neural Face Editing with Intrinsic Image Disentangling

Authors:
  • ZSZhixin Shu
    Zhixin Shu
  • EYErsin Yumer
    Ersin Yumer
  • SHSunil Hadap
    Sunil Hadap
  • KSKalyan Sunkavalli
    Kalyan Sunkavalli
  • ESEli Shechtman
    Eli Shechtman
  • DSDimitris Samaras
    Dimitris Samaras
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Abstract
Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other - a process that is tedious, fragile, and computationally intensive. In this paper, we propose an end-to-end generative adversarial network that infers a face-specific disentangled representation of intrinsic face properties, including shape (i.e. normals), albedo, and lighting, and an alpha matte. We show that this network can be trained on “in-the-wild” images by incorporating an in-network physically-based image formation module and appropriate loss functions. Our disentangling latent representation allows for semantically relevant edits, where one aspect offacial appearance can be manipulated while keeping orthogonal properties fixed, and we demonstrate its use for a number offacial editing applications.

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