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Lifespan Age Transformation Synthesis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12351)

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.

Notes

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.

Supplementary material

504443_1_En_44_MOESM1_ESM.pdf (32.2 mb)
Supplementary material 1 (pdf 32931 KB)

Supplementary material 2 (mp4 171 KB)

Supplementary material 3 (mp4 238 KB)

Supplementary material 4 (mp4 218 KB)

Supplementary material 5 (mp4 311 KB)

Supplementary material 6 (mp4 153 KB)

Supplementary material 7 (mp4 160 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of WashingtonSeattleUSA
  2. 2.Stanford UniversityStanfordUSA
  3. 3.Adobe ResearchSeattleUSA

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