Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Today we are going to see if a virtual AI character can learn, or perhaps even invent
these amazing signature moves.
And this is a paper that was written by Sebastian Starke and his colleagues.
He is a recurring scientist on this series, for instance, earlier he wrote this magnificent
paper about Dribbling AI characters.
Look.
The key challenge here was that we were given only 3 hours of unstructured motion capture
data.
That is next to nothing, and from this next to nothing, it not only learned these motions
really well, but it could weave them together, even when a specific movement combination
was not present in this training data.
But, as these motions are created by human animators, and may show at least three problems.
One, the training data may contain poses that don’t quite adhere to the physics of a real
human character.
Two, it is possible that the upper body does something that makes sense, the lower body
also does something that makes sense, but the whole thing, put together, does not make
too much sense anymore.
Or, three, we may have these foot sliding artifacts that you see here.
These are more common than you might first think, here is an example of it from a previous
work, and, look, nearly all of the previous methods struggle with it.
Now, this new work uses 20 hours of unstructured training data.
Now, remember, the previous one only used 3, so we rightfully expect that by using more
information, it can also learn more.
But the previous work was already amazing, so, what more can we really expect this new
one to do?
Well, it can not only learn these motions, weave together these motions like previous
works, but, hold on to your papers, because it can now also come up with novel moves as
well!
Wow.
This includes new attacking sequences, and combining already existing attacks with novel
footwork patterns.
And it does all this spectacularly well.
For instance, if we show it how to have its guard up, and how to throw a punch, what will
it learn?
Get this, it will keep its guard up while throwing that punch.
And it not only does that in a realistic, fluid movement pattern, but it also found
out about something that has strategic value.
Same with evading an attack with some head movement and counterattacking.
Loving it.
But how easy is it to use this?
Do we need to be an AI scientist to be able to invoke these amazing motions?
Well, if you have been holding on to your papers so far, now, squeeze that paper, and
look here.
Wow.
You don’t need to be an AI scientist to play with this!
Not at all!
All you need is a controller to invoke these beautiful motions.
And all this runs in real time.
My goodness!
For instance, you can crouch down and evade a potential attack by controlling the right
stick, and launch a punch in the meantime.
Remember, not only both halves have to make sense separately, the body motion has to make
sense as a whole.
And it really does, look at that.
And here comes the best part, you can even assemble your own signature attacks, for instance,
perform that surprise spinning backfist, an amazing spin kick, and, yes!
You can even go full Karate Kid with that crane kick.
And as a cherry on top, the characters can also react to the strikes, clinch, or even
try a takedown.
So, with that, there we go, we are again, one step closer to having access to super
realistic motion techniques for virtual characters, and all we need for this is a controller.
And remember, all this already runs in real time!
Another amazing SIGGRAPH paper from Sebastian Starke.
And get this, he is currently a 4th year PhD student and already made profound contributions
to the industry.
Huge congratulations on this amazing achievement.
What a time to be alive!
Thanks for watching and for your generous support, and I'll see you next time!