Free AI-generated headshots put stock photo companies on notice
theverge.com
theverge.com
These pictures suffer from a common newbie-artist problem!
One of the concepts of early stage drawing I noticed is "symbol drawing". This is where a person knows how an eye should look, and where an eye should go on the face, and so draws an eye on a face-shaped oval. They repeat this for the other eye, a nose, a mouth, etc. They are often sad with the results.
A person who practices incorrectly will try to do photorealistic eyes, noses, mouths, etc but use the same 2D composition technique. No matter how realistically they can render components, their composition is off because they are ignoring the structure of a face that causes subtle differences in the shape of each of the components when projected onto a sheet of paper.
Our mind is so capable of 3D modelling that when we look at a face we don't notice the changes in perspective leading to changes in component shape and shading. The great artist skill is to build up those 3D to 2D projections and avoid the 2D composition (except in stylistic choices).
I suspect that the networks rendering these faces, and the underlying biological phenomena they are designed against, are "symbol" recognizers, rather than "structural" recognizers.
Is it also difficult to get the face to align with the teeth? Nearly all of their pearly whites feel eerily disconnected to me.
A biological response might be "misshapen eye ... likely parasite detected, avoid this person" or "mismatched emotional cues ... likely brain damage / person unpredictable, avoid"
Definitely the case for this guy https://cdn.vox-cdn.com/uploads/chorus_asset/file/19216620/0...
Moving your left eye, nose, mouth, and right ear independently of each other is fundamental to acting?
"Unexpected" is a milder term than "never before seen on a human seen, nor interpolated".
Buddy, I appreciate it. You know how hard it is when you’re surrounded by doofuses who can’t even read well enough to tell the difference between race and class and who don’t even understand how to read in good faith much less do it, and stomp all over themselves trying to epic own people with ‘facts’ that are just straw men created from their flawed understanding of existence outside of their bubble? It’s tough, not gonna lie.
That being said, if you scroll through thousands of these, what you are saying does happen often, but even more often, the faces look completely real and I would have no problem believing they were anything but.
Maybe I missed your point though....
I wonder what causes it.
I don't know why it's so common to have an arbitrary nonsense "sticker" on a face though, I wonder if it's because input photos were trunacted at edges and the learner tried to model the edge as a facial feature instead of pruning it's knowledge to a safe interior of the photo.
I suppose that depends on who the viewer is. For example, everyone at work was blown away with how hard it was to pick the real face here: http://www.whichfaceisreal.com/index.php
However, I went through about 50 in under a minute and got every one correct.
Programmers spend their time thinking in abstractions and models, rather than appreciating things as they are, as an artist must (see Drawing on the Right Side of the Brain -- https://www.drawright.com/theory). I wouldn't be surprised if more artsy types have a much easier time recognizing these fake photos, despite all the component pieces roughly being where they should be.
The "right way" to do high-level structure for images is a fruitful active area of investigation which I expect will probably be tackled in the next 18 months.
Bold prediction. I wouldn't be surprised if this is an open problem in 18 years.
For each problem there are a few sub-problems and a "right way" to do each of them where a "right way" corresponds to an algorithm which efficiently tackles some element of the implicit structure of the problem.
I think within 5 years we will have made great progress on algorithmically discovering these "right ways" across many problem domains. I think both differentiable neural architecture search[0] and the MAML with implicit gradients[1] stuff is very interesting from this perspective.
It's nonsense to learn 2D projections directly from 2D projections, because small errors in the 2D projection can become catastrophic errors in the 3D model that humans use to interpret a 2D photo.
Also, quality data is always the hard part here, so we might be able to bootstrap some of this structure learning with 3D models created for games, projected as 2D, and use that as transfer learning somehow.
> A person who practices incorrectly
So how does one practice correctly?
For example:
https://www.youtube.com/watch?v=qX8AIRsFmTA
or use the rotation of a real face as a guide:
https://www.youtube.com/watch?v=p1b5aiTrGzY
it's not hyper-realistic but it's getting better and better.
Then, do still life sketches of what you sculpt, no matter how bad. This seemed to reinforce the other direction for me.
That and play Descent 2.
If an ai company looked at thousands of examples of commercial, copyrighted stock photos, then created an ai that would make similar stock photos, but have a method that prunes or selects generated photos that are sufficiently distant from any example as no not be obviously derivative, could they conceal their “theft” and sell their stock photos free of legal risk?
More generally, can AI wash itself clean of copyright infringement by showing that it was “inspired” but not derivative? I guess a judge could compel you to reveal your training set, but at some point do you think there will be general ai that can have the argument that it only seeks “inspiration” and does not “knock off”?
if a human did it, it would obviously be clean of copyright infringement. So why is it any different if an AI did it?
Copyright was created as a response to the printing press being able to copy and distribute. However, with the advent of AI, this may no longer be effective. But to use existing copyright laws to control the output of an AI is wrong. May be new laws are needed. Until then, i dont think copyright infringement apply to the output of an AI if the output would not already infringe copyright had it been done by a human.
I tried to do some research into this awhile ago. What I found is that you (generally, kind of) are able to use copyrighted work to make another work as a "transformative work". For example: you can look at an image of a person and use that as _inspiration_ to draw the same person (as long as you aren't tracing). However! that's still kind of a gray area.
ALSO: how does that apply if you are using the exact pixels of 1,000,000 images to make new ones? I don't think anyone has a definitive answer yet.
My guess is that it will have to be decided in some high profile court case before we get real answers :)
Imagine if Micheal Jackson made one album, but a week later AI’s made thousands of albums that sounded just as good and original (or better!) but were categorized as inspirations. Imagine how that would change the incentives of creation to know it will be consumed by the hive mind in mere moments.
I don’t think it would change incentives much. Michael Jackson is a brand. Having procedurally generates Michael Jacksonish type music might be nice for elevator companies, but won’t impact his ticket sales that much. There are always lots of similar bands trying to emulate the most popular. Sometimes they success (Creed v. Pearl Jam) but many times the only way I would learn about them is by finding them in the dollar bin. I’m not sure what the present day equivalent is of the dollar CD box.
We'll need to train a copyright-evaluating AI based on human judgments.
It might depend on if you have deep enough pockets to prove you are not violating copyright.
https://www.federalregister.gov/documents/2019/08/27/2019-18...
I think we should err on the side of non-infringement. After all, this is how our real brains work. As long as the works are perceived as nonrelated, it shouldnt matter if they come from someone else s dataset. The similarity could be put to the test by doing a double blind test with a group of humans
This is useful, makes promotional/demo material more fun
Little tutorial previously posted on HN teaching you how to spot the fakes. The tell is usually around the hairline for me.
Most noticeably in 3/4 of the images in the article, the subject's gaze does not work out. Their eye lines never converge.
That's likely exactly how these pictures were generated.
Generative adversarial neural networks (the typical approach for this type of problem) have two nets that compete against each other.
One net tries to generate images that look like the sample data, the other one tries to tell them apart.
The use of these specific ones would be easily detected, since the dataset is available to everyone and companies can cross-reference. However, if the program becomes commercially available, unique fakes can easily be created.
PS: Linking from your already downloaded url will generally be tied to your account session.
0: https://drive.google.com/file/d/1JKoYoqMIbsaj0jZNvpUzFQCHrWP...
Single handedly, even. This is something one person could do on their own.
And yet they didn't even bother to try.
If they really intend to sell such images, it's extremely unprofessional to leave the botched examples in the mix, despite efforts to rationalize why they should be included.
Based on that, I suspect it's one person acting alone, and it's a get rich quick effort to take the money and run.
[1] https://thispersondoesnotexist.com/ [2] https://news.ycombinator.com/item?id=19144280
> Zhabinskiy is keen to emphasize that the AI used to generate these images was trained using data shot in-house, rather than using stock media or scraping photographs from the internet. “Such an approach requires thousands of hours of labor, but in the end, it will certainly be worth it!” exclaims an Icons8 blog post.