Numerous style transfer methods which produce artistic styles of portraits have been proposed to date. However, the inverse problem of converting the stylized portraits back into realistic faces is yet to be investigated thoroughly. Reverting an artistic portrait to its original photo-realistic face image has potential to facilitate human perception and identity analysis. In this paper, we propose a novel Face Destylization Neural Network (FDNN) to restore the latent photo-realistic faces from...[Show more]
|Collections||ANU Research Publications|
|Source:||DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications|
|01_Shiri_Face_Destylization_2017.pdf||722.98 kB||Adobe PDF||Request a copy|
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