These faces are “ambigrams”: images that are legible both upside-down and right-side up. Created with a machine learning system, they may be displayed in any orientation. In this project, a collection of 55 such ambigrammatic faces have been generated in high resolution, and printed as a limited edition deck of poker-sized cards.
…Ultimately, our Ambigrammatic Figures were synthesized with the StyleGAN2 generative adversarial network, using pre-trained weights from the Flickr-Faces-HQ Dataset (FFHQ), and enhanced using the waifu2x super-resolution library.
Our work uses the StyleGAN2“projection” technique, in which the GAN attempts to find a given face in its latent space, starting its search from a “generic” “neutral” face located at the origin.
We provide the StyleGAN an upside-down face as a query—and the projector tries its best to find it, but can never serve a perfect match, because it has only been trained on exclusively right-side-up faces. In short, the GAN projector finds upside-down faces in the latent space (or “generatable manifold”) of right-side up faces. Through the struggle to match an upside-down face using right-side-up ones, the GAN tends to converge on a face that can be looked at both ways.