Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Here you see people that don’t exist.
How can that be?
Well, they don’t exist because these images were created with a neural network-based learning
method by the name StyleGAN2, which can not only create eye-poppingly detailed looking
images, but it can also fuse these people together, or generate cars, churches, horses,
and of course, cats.
The even cooler thing is that many of these techniques allow us to exert artistic control
over these images.
So how does that happen?
How do we control a neural network?
It happens through exploring latent spaces.
And what is that?
A latent space is a made-up place where we are trying to organize data in a way that
similar things are close to each other.
What you see here is a 2D latent space for generating different fonts.
It is hard to explain why these fonts are similar, but most of us would agree that they
indeed share some common properties.
The cool thing here is that we can explore this latent space with our cursor, and generate
all kinds of new fonts.
You can try this work in your browser, the link is available in the video description.
And, luckily, we can build a latent space, not only for fonts, but, for nearly anything.
I am a light transport researcher by trade, so in this earlier paper, we were interested
in generating somewhat hundreds of variants of a material model to populate this scene.
In this latent space, we can concoct all of these really cool digital material models.
A link to this work is also available in the video description.
So let’s recap - one of the cool things we can do with latent spaces is generate new
images that are somewhat similar.
But there is a problem.
As we go into nearly any direction, not just one thing, but many things about the image
change.
For instance, as we explore the space of fonts here, not just the width of the font changes,
everything changes.
Or if we explore materials here, not just the shininess or the colors of the material
change, everything changes.
This is great to explore if we can do it in real time.
If I change this parameter, not just the car shape changes, the foreground changes, the
background changes…again, everything changes!
So, these are nice and intuitive controls, but not interpretable controls.
Can we get that somehow?
The answer is, yes, not everything must change, this previous technique is based on StyleGAN2
and is called StyleFlow, and it can take an input photo of a test subject, and edit a
number of meaningful parameters.
Age, expression, lighting, pose, you name it.
For instance, it could also grew Elon musk a majestic beard.
And that’s not all, because Elon Musk is not the only person who got a beard.
Look, this is me here, after I got locked up for dropping my papers.
And I spent so long in there, that I grew a beard.
Or I mean, this neural network gave me one.
And since the punishment for dropping your papers is not short…in fact, it is quite
long…this happened.
Ouch.
I hereby promise to never drop my papers, ever again.
You will also have to hold on to yours too, so stay alert.
So, apparently interpretable controls already exist.
And I wonder, how far can we push this concept?
Beard or no beard is great, but what about cars, what about paintings?
Well, this new technique found a way to navigate these latent spaces and introduces 3 amazing
new examples of interpretable controls that I haven’t seen anywhere else yet.
One, it can change the car geometry.
We can change the sportiness of a car, and even ask the design to be more or less boxy.
Note that there is some additional damage here, but we can counteract that by changing
the foreground to our taste, for instance, add some grass in there.
Two, it can repaint paintings.
We can change the roughness of the brush strokes, simplify the style
or even rotate the model.
This way, we can create or adjust a painting without having to even touch a paintbrush.
Three, facial expressions.
First, when I started reading this paper, I was a little suspicious.
I have seen these controls before so I looked at it like this, but as I saw how well it
did, I went more… like this.
And this paper can do way more, for instance, it can add lipstick, change the shape of the
mouth or the eyes, and do all this with very little collateral damage to the remainder
of the image.
Loving it.
It can also find and blur the background similarly to those amazing portrait mode photos that
newer smartphones can do.
And, of course, it can also do the usual suspects.
Adjusting the age, hairstyle, or growing a beard.
So with that, there we go, now, with the power of neural network-based learning methods,
we can create new car designs, can repaint paintings without ever touching a paintbrush,
and give someone a shave.
It truly feels like we are living in a science fiction world.
What
a time
to be alive!
Thanks for watching and for your generous support, and I'll see you next time!