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Several research groups have shown that Generative Adversarial Networks(GANs) can generate photo-realistic images in recent years. Using the GANs, amap is created between a latent code and a photo-realistic image. This processcan also be reversed: given a photo as input, it is possible to obtain thecorresponding latent code. In this paper, we will show how the inversionprocess can be easily exploited to interpret the latent space and control theoutput of StyleGAN2, a GAN architecture capable of generating photo-realisticfaces. From a biological perspective, facial features such as nose size dependon important genetic factors, and we explore the latent spaces that correspondto such biological features, including masculinity and eye colour. We show theresults obtained by applying the proposed method to a set of photos extractedfrom the CelebA-HQ database. We quantify some of these measures by utilizingtwo landmarking protocols, and evaluate their robustness through statisticalanalysis. Finally we correlate these measures with the input parameters used toperturb the latent spaces along those interpretable directions. Our resultscontribute towards building the groundwork of using such GAN architecture inforensics to generate photo-realistic faces that satisfy certain biologicalattributes.

A naive method to discover directions in the StyleGAN2 latent space