Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér.
In this series, we talk about research on all kinds of physics simulations, including
fluids, collision physics, and we have even ventured into hair simulations.
If you look here at this beautiful footage, you may be surprised to know how many moving
parts a researcher has to get right to get something like this.
For instance, some of these simulations have to go down to the level of computing the physics
between individual hair strands.
If it is done well, like what you see here from our earlier episode, these simulations
will properly show us how things should move, but that’s not all - there is also an abundance
of research works out there on how they should look.
And even then, we’re not done, because before that, we have to take a step back and somehow
create these digital 3D models that show us the geometry of these flamboyant hairstyles.
Approximately 300 episodes ago, we talked about a technique that took a photograph as
an input, and created a digital 3D model that we can use in our simulations and rendering
systems.
It had a really cool idea where it initially predicted a coarse result, and then, this
result was matched with the hairstyles found in public data repositories, and the closest
match was presented to us.
Clearly, this often meant that we got something that was similar to the photograph, but often
not exactly the hairstyle we were seeking.
And now, hold on to your papers because this work introduces a learning-based framework
that can create a full reconstruction by itself, without external help, and now, squeeze that
paper, because it works not only only for images, but for videos too!
Woohoo!
It works for shorter hairstyles, long hair, and even takes into consideration motion and
external forces as well.
The heart of the architecture behind this technique is this pair of neural networks,
where the one above creates the predicted hair geometry for each frame, while the other
tries to look backwards in the data and try to predict the appropriate motions that should
be present.
Interestingly, it only needs two consecutive frames to make these predictions, and adding
more information does not seem to improve its results.
That is very little data.
Quite remarkable.
Also, note that there are a lot of moving parts here in the full paper, for instance,
this motion is first predicted in 2D, and is then extrapolated to 3D afterwards.
Let’s have a look at this comparison - indeed, it seems to produce smoother and more appealing
results than this older technique.
But if we look here, this other method seems even better, so what about that?
Well, this method had access to multiple views of the model, which is significantly more
information than what this new technique has that only needs a simple monocular 2D video
from our phone, or from the internet.
The fact that they are even comparable is absolutely amazing.
If you have a look at the paper, you will see that it even contains a hair growing component
in this architecture.
And as you see, the progress in computer graphics research is also absolutely amazing.
And we are even being paid for
doing this.
Unreal.
Thanks for watching and for your generous support, and I'll see you next time!