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Lifespan Age Transformation Synthesis

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

We address the problem of single photo age progression and regression—the prediction of how a person might look in the future, or how they looked in the past. Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process. This limits the applicability of previous methods to aging of adults to slightly older adults, and application of those methods to photos of children does not produce quality results. We propose a novel multi-domain image-to-image generative adversarial network architecture, whose learned latent space models a continuous bi-directional aging process. The network is trained on the FFHQ dataset, which we labeled for ages, gender, and semantic segmentation. Fixed age classes are used as anchors to approximate continuous age transformation. Our framework can predict a full head portrait for ages 0–70 from a single photo, modifying both texture and shape of the head. We demonstrate results on a wide variety of photos and datasets, and show significant improvement over the state of the art.

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Notes

  1. 1.

    https://www.appen.com/.

  2. 2.

    https://www.faceplusplus.com/.

  3. 3.

    https://www.faceapp.com/.

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Acknowledgements

We wish to thank Xuan Luo and Aaron Wetzler for their valuable discussions and advice, and Thevina Dokka for her help in building the FFHQ-Aging dataset. This work was supported in part by Futurewei Technologies. O.F. was supported by the Brown Institute for Media Innovation. All images in this manuscript are licensed under creative commons license and were taken from the FFHQ dataset.

Author information

Authors and Affiliations

  1. University of Washington, Seattle, USA

    Roy Or-El, Soumyadip Sengupta & Ira Kemelmacher-Shlizerman

  2. Stanford University, Stanford, USA

    Ohad Fried

  3. Adobe Research, Seattle, USA

    Eli Shechtman

Authors
  1. Roy Or-El
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  2. Soumyadip Sengupta
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  3. Ohad Fried
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  4. Eli Shechtman
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  5. Ira Kemelmacher-Shlizerman
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Corresponding author

Correspondence to Roy Or-El .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Or-El, R., Sengupta, S., Fried, O., Shechtman, E., Kemelmacher-Shlizerman, I. (2020). Lifespan Age Transformation Synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_44

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  • DOI: https://doi.org/10.1007/978-3-030-58539-6_44

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