There has been tremendous progress in generating realistic faces with high fidelity over the past few years. Despite this progress, a crucial question remains unanswered: "Given a generative face model, how many unique identities can it generate?" In other words, what is the biometric capacity of the generative face model? A scientific basis for answering this question will benefit evaluating and comparing different generative face models and establish an upper bound on their scalability. This paper proposes a statistical approach to estimate the biometric capacity of generated face images in a hyperspherical feature space. We employ our approach on multiple generative models, including unconditional generators like StyleGAN, Latent Diffusion Model, and "Generated Photos," as well as DCFace, a class-conditional generator. We also estimate capacity w.r.t. demographic attributes such as gender and age. Our capacity estimates indicate that (a) under ArcFace representation at a false acceptance rate (FAR) of 0.1%, StyleGAN3 and DCFace have a capacity upper bound of $1.43\times10^6$ and $1.190\times10^4$, respectively; (b) the capacity reduces drastically as we lower the desired FAR with an estimate of $1.796\times10^4$ and $562$ at FAR of 1% and 10%, respectively, for StyleGAN3; (c) there is no discernible disparity in the capacity w.r.t gender; and (d) for some generative models, there is an appreciable disparity in the capacity w.r.t age. Code is available at https://github.com/human-analysis/capacity-generative-face-models.

For the past two years, I've been following a woman around the internet. It sounds ominous, I know, but hear me out. Her name is Albertina Geller, and I first stumbled across her online in October 2020, on LinkedIn. She'd listed herself as a "self-employed freelancer" in Chicago. I'm also a self-employed freelancer, so we had that in common. In her bio, she said that "I learn & teach people how to be healthy, balance their gut and improve their immune system for healthy living." I've had some gut and immune-system issues myself. It was a connection practically written in the stars. But I have to admit that what first interested me about her -- what led me to spend two years tracking her, at a distance -- wasn't our shared interests. Her LinkedIn photo was a straight-on headshot of a white woman, mid- to late 20s, with a pale complexion and lightly rosy cheeks. She had shoulder-length blond hair, swept neatly to one side.

First we had deepfakes, which could glue someone's face onto someone else's body. Then we had This Person Does Not Exist, which created people on a website every time you refreshed the page. Then we had Generated Photos, a commercial stock photography site, built entirely from AI-generated humans. Generating realistic-looking people has been one of the biggest challenges in visual AI, but researchers are mastering the technique quickly. The latest example: Generated Photos--which currently does $15,000 a month in revenue selling a library of AI-generated stock models, according to the company--has released an update that not only generates an AI-built human on demand but also lets you position it.

Visual 1st, the annual Silicon-Valley imaging conference for industry leaders and upstarts, once again brought together a worldwide audience for a day-and-a-half executive conference. The event, held Oct. 2-3 at the Golden Gate Club in San Francisco, addresses topics as far-reaching as artificial intelligence and as every day as printing. As with most conferences, the real meat of the event is the hallway discussions and informal meetings over a beer or wine at the reception. Below are some photos from the conference, courtesy of sponsor, Sweet Escapes. Each year, a panel of high-powered industry experts presented the four Visual 1st Awards to the most outstanding among 30 products competing in this year's show-and-tell demo sessions.

Generated Photos is a collection of 100,000 human faces, all free to download and use for any purpose. These people are beautiful, diverse, and ready to show up in your next ad campaign. Oh, and none of them are real. They've been generated by artificial intelligence. Most look indistinguishable from real human beings, but they are all just very cleverly arranged pixels, sorted by a machine.

A database of more than 100,000 images of people has been created - but none of them are real. A team of artists, AI experts and engineers teamed up on the project to create the ultra-realistic images. It includes different sexes, races and ages and none of the people are real, but could easily be mistaken for a legitimate portrait. The eerie headshots appear perfect in many cases, but there are often minor glitches, including eye sockets on foreheads, funky teeth and weird ears. A team of artists, AI experts and engineers teamed up on the project to create the ultra-realistic images.

For most design projects and presentations, a common part is searching for photos of people. What designers strive for is quality images that are also diverse, free and legal. What's more, it often means spending precious time looking for the proper photo content all around the Web. Today it got much easier: new free resource Generated Photos has just been launched to help. It shares a diverse library of 100,000 incredibly realistic faces created by artificial intelligence.