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CONFIG: Controllable Neural Face Image Generation

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12356)

Abstract

Our ability to sample realistic natural images, particularly faces, has advanced by leaps and bounds in recent years, yet our ability to exert fine-tuned control over the generative process has lagged behind. If this new technology is to find practical uses, we need to achieve a level of control over generative networks which, without sacrificing realism, is on par with that seen in computer graphics and character animation. To this end we propose ConfigNet, a neural face model that allows for controlling individual aspects of output images in semantically meaningful ways and that is a significant step on the path towards finely-controllable neural rendering. ConfigNet is trained on real face images as well as synthetic face renders. Our novel method uses synthetic data to factorize the latent space into elements that correspond to the inputs of a traditional rendering pipeline, separating aspects such as head pose, facial expression, hair style, illumination, and many others which are very hard to annotate in real data. The real images, which are presented to the network without labels, extend the variety of the generated images and encourage realism. Finally, we propose an evaluation criterion using an attribute detection network combined with a user study and demonstrate state-of-the-art individual control over attributes in the output images.

Keywords

Neural rendering Face image manipulation GAN 

Notes

Acknowdledgments

The authors would like to thank Nate Kushman for helpful discussions and suggestions.

Supplementary material

504452_1_En_18_MOESM1_ESM.pdf (9.8 mb)
Supplementary material 1 (pdf 10079 KB)

Supplementary material 2 (mp4 36843 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.MicrosoftCambridgeUK

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