How Does AI Generate Faces?

Lately, artificial intelligence (AI) has been a leading topic of debate among artists and creatives for its immediate impact on the art world. AI face generation has become one of the stand-out features of AI tools, and in this article, we will explore how AI generates faces and how an AI face creator actually works.

What Is AI?

To understand what an AI Generation tool for faces is, we will need to talk about AI as a whole briefly.

AI is a simulation of what we see as an approximation of human intelligence. The primary attributes important to AI are learning, reasoning, and perception, all of which are powered by the underlying computing technology such as a processor. 

Some AI is purpose-built to be single-task oriented, while some of the most advanced AI can carry out tasks that are complex and even learn while doing so through a procedure known as machine learning (ML). This results in code that can follow specific paths and improve upon itself within predetermined conditions. 

How Does an AI Generate Faces?

AI-generated images, known as “Deepfakes” in some cases, have seen an exponential level of usage and appeal in the special effects space. AI-generated faces can create a fictional person, swap one person's identity with another, or alter what an existing person currently looks like. This means that AI-generated faces have the power to entertain audiences as well as deceive them. These abilities have become so commonplace that now most people can easily learn how to create faces with StyleGAN.

Generative adversarial networks (GANs) is one of the most popular mechanisms used to synthetize content, and here’s how it works.

  • GANs have two neural networks—a generator and a discriminator—which are pinned against each other.
  • To synthesise an image of a fictional person, GANs begin by randomly selecting an array of pixels out of which they learn how to iteratively synthesise a realistic face.
  • With each iteration, the discriminator can essentially learn how to distinguish a synthesised face from numerous real faces.
  • At the point in time when this new face becomes distinguishable from real faces, the generator gets penalised by the discriminator.
  • Throughout the numerous iterations, all of which happen quickly through the power of the underlying hardware it works upon, the generator can learn how to create faces that are more realistic until the discriminator is unable to distinguish them from their real counterparts. 

There are evolutions of these algorithms, such as VQGAN and CLIP, which, when combined, can provide various AI-generated artworks. Anyone can run this code in order to generate their own art. If you want to learn more about this, be sure to check out our Artbreeder tutorial.

Final Thoughts

AI has come a long way in its availability and ability. When properly implemented, AI-generated faces can provide companies and artists with new resources to easily and efficiently complete projects.