Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Here you see people that don’t exist.
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.
This is quite convincing so is there anything else to do in human face generation?
Are we done?
Well, this footage from a new paper may give some of it away.
If you have been watching this series for a while, you know that of course, researchers
always find a way to make these techniques better, we always say, two more papers down
the line and it will be way better.
And now, we are one more paper down the line, so let’s see together if there has been
any improvement.
This new 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.
Now note that there were other techniques that could pull this off, but the key improvements
here is that one, we can perform many sequential changes, and two, is does all this while remaining
faithful to the original photo.
And hold on to your papers, because three, it can also help Elon Musk to grow a majestic
beard.
Believe it or not, you will also see a run of this algorithm on me as well at the end
of the video.
First, let’s adjust the lighting a little, and now, it’s time for that beard.
Loving it.
Now, a little aging, and one more beard.
It seems to me that this beard is different from the young man beard, which is nice attention
to detail.
And note that we have strayed very far from the input image, but if you look at the intermediate
states, you see that the essence of the test subject is indeed quite similar.
This Elon is still Elon.
Note that these results are not publicly available and were made specifically for us, so you
can only see this here on Two Minute Papers.
That is quite an honor, so thank you very much to Rameen Abdal, the first author of
the paper for being so kind.
Now, another key improvement in this work is that we can change one of these parameters
with little effect on anything else.
Have a look at this workflow and see how well we can perform these sequential edits.
These are the source images, and the labels showcase the one variable change for each
subsequent image, and you can see how amazingly surgical the changes are.
Witchcraft.
If it says that we change the facial hair, that’s the only change I see in the output.
And just think about the fact that these starting images are real photos, but the outputs of
the algorithm are made-up people that don’t exist.
And notice that the background is also mostly left untouched.
Why does that matter?
You will see in the next example.
So far, so good, but this method can do way more than this.
Now let’s edit, not people, but cars.
I love how well the color and pose variations work.
Now, if you look here, you see that there is quite a bit of collateral damage, as not
only the cars, but the background is also changing, opening up the door for a potentially
more surgical followup paper.
Make sure to subscribe and hit the bell icon to get notified when we cover this one more
paper down the line.
And now, here is what you have been waiting for, we’ll get hands-on experience with
this technique, where I shall be the subject of the next experiment.
First, the algorithm is looking for high-resolution frontal images, so for instance, this would
not work at all, we would have to look for a different image.
No matter, here’s another one where I got locked up for reading too many papers.
This could be used as an input for the algorithm, and, look.
This image looks a little different.
Why is that?
It is because StyleGAN2 runs an embedding operation on the photo before starting its
work.
This is an interesting detail that we only see if we start using the technique ourselves.
And now, come on, give me that beard.
Ho-ho-hoo!
Loving it!
What do you think, whose beard is better, Elon’s or mine?
Let me know in the comments below.
And now, please meet old man Károly, he will tell you that papers were way better back
in his day, and the usual transformations.
But also note that as a limitation, we had quite a bit of collateral damage for the background.
This was quite an experience.
Thank you so much.
And we are not done yet because this paper just keeps on giving.
It can also perform attribute transfer.
What this means is that we have two input photos, this will be the source image, and
we can choose a bunch of parameters that we would like to extract from it.
For instance, the lighting and pose can be extracted through attribute transfer, and
it seems to even estimate the age of the target subject and change the source to match it
better.
Loving it.
The source code for this technique is also available and make sure to have a look at
the paper, it is very thoroughly evaluated, the authors went the extra mile there and
it really shows.
And I hope that you now agree, even if there is a technique that appears quite mature,
researchers always find a way to further improve it.
And who knew, it took a competent AI to get me to grow a beard.
Totally worth it.
What
a time
to be alive!
Thanks for watching and for your generous support, and I'll see you next time!