Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains

11 Oct 2020  ·  Justin N. M. Pinkney, Doron Adler ·

GANs can generate photo-realistic images from the domain of their training data. However, those wanting to use them for creative purposes often want to generate imagery from a truly novel domain, a task which GANs are inherently unable to do. It is also desirable to have a level of control so that there is a degree of artistic direction rather than purely curation of random results. Here we present a method for interpolating between generative models of the StyleGAN architecture in a resolution dependent manner. This allows us to generate images from an entirely novel domain and do this with a degree of control over the nature of the output.

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Datasets


Introduced in the Paper:

Ukiyo-e Faces

Used in the Paper:

FFHQ

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Methods