research-article

Face identity disentanglement via latent space mapping

Authors:

Daniel Cohen-OrAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 39, Issue 6

Article No.: 225, Pages 1 - 14

https://doi.org/10.1145/3414685.3417826

Published: 27 November 2020 Publication History

Abstract

Learning disentangled representations of data is a fundamental problem in artificial intelligence. Specifically, disentangled latent representations allow generative models to control and compose the disentangled factors in the synthesis process. Current methods, however, require extensive supervision and training, or instead, noticeably compromise quality.

In this paper, we present a method that learns how to represent data in a disentangled way, with minimal supervision, manifested solely using available pre-trained networks. Our key insight is to decouple the processes of disentanglement and synthesis, by employing a leading pre-trained unconditional image generator, such as StyleGAN. By learning to map into its latent space, we leverage both its state-of-the-art quality, and its rich and expressive latent space, without the burden of training it.

We demonstrate our approach on the complex and high dimensional domain of human heads. We evaluate our method qualitatively and quantitatively, and exhibit its success with de-identification operations and with temporal identity coherency in image sequences. Through extensive experimentation, we show that our method successfully disentangles identity from other facial attributes, surpassing existing methods, even though they require more training and supervision.

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Index Terms

Face identity disentanglement via latent space mapping
1. Computing methodologies
  1. Computer graphics
  2. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
      2. Neural networks

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 39, Issue 6

December 2020

1605 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3414685

Editor:
Karol Myszkowski
MPI Informatik

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Published: 27 November 2020

Published in TOG Volume 39, Issue 6

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