Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Today we are going to talk about an amazing new technique to make these beautiful, but
very expensive computer simulations cheaper. How much cheaper? Well, I will tell you in a moment.
You see, around here, we discuss research papers like this and this. We talk a great
deal about how difficult and computationally expensive it is to simulate all this physics,
but that’s not the only thing that needs to be done here. All this data also has to be
stored too! That is a ton of geometry information. How much exactly? Well,
15 gigabytes for a simulation is not uncommon, and my fluid simulation that you see here took well
over a hundred gigabytes of space to store. Some of the more extreme examples can fill
several hard drives for only a minute of physics simulation data. That is an insane amount of data.
So, we need something to help compressing down all this data. And the weapon of choice for working
with this kind of volumetric data is often a tool called OpenVDB. It has this hierarchical
structure within, and scientists at NVIDIA had a crazy idea. They said, how about exchanging this
with all of these amazing learning-based methods that you are hearing about everywhere? Well,
okay, but why just throw a neural network at the problem? Is this really going to help?
Well, maybe. You see, neural networks have exceptional compression capabilities. For
instance, we have seen with their earlier paper that such an AI can help us transmit video data
of us by, get this: only taking the first image from the video, and just a tiny bit of extra data,
and they throw away the entire video afterwards! So, can these techniques do some more magic in
the area of physics simulations? Those are much more complex, but who knows, let’s see!
And now, hold on to your papers, because it looks like this. Now that is all well and good,
but here is the kicker, it takes about 95% less storage than the previous method. Yes, that is 20x
compression while the two pieces of footage still look the same. Wow. That is absolutely incredible.
And the previous OpenVDB solution is not just for some hobby projects,
it is used in many of the blockbuster feature-length movies you all know and
love. These movies won many-many academy awards. And, we are not done yet. Not
even close - it gets even better! If you think 20x is the best it can do, now look,
we also have 50x here. And sometimes, it gets up to even 60x smaller. My goodness!
And this new NeuralVDB technique from NVIDIA can be used for not only these amazing movies,
but for everything else OpenVDB was useful for. Oh yes! That means that
we can go beyond these movies, and use it for scientific and even industrial visualizations.
They also have the machinery to read and process all this data with a graphics card,
therefore it not only decreases the memory footprint of these techniques,
but it even improves how quickly they run. My mind is officially blown. So much progress in
so little time. And this is my favorite kind of research, and that is when we look into the
intersection of computer graphics and AI. That’s where we find some of the most beautiful works.
And don’t forget, these applications are plentiful. This could also be used with
FourCastNet, which is a physics model that can predict outlier weather events. The
coolest part about this work is that it runs not in a data center anymore,
but today, it runs on just one NVIDIA graphics card. And with NeuralVDB,
it will require even less memory to do so. And it will help with all of these
absolute miracles of science that you see here too. So, all of these could run with
an up to 60 times smaller memory footprint? That is incredible. What a time to be alive!
Huge congratulations to Doyub Kim, the first author of the paper and his team for this amazing
achievement. And once again, you see the power of AI, the power of the papers, and tech transfer at
the same time. And, since NVIDIA has an excellent record in putting these tools into the hands of
all of us, for instance, they did it with the previous incarnation of this technique, NanoVDB,
I am convinced that we are all going be able to enjoy this amazing paper soon. How cool is that?
So, does this get your mind going? What would you use this for? Let me know in the comments below!
Thanks for watching and for your generous support, and I'll see you next time!