# NVIDIA’s New AI: Gaming Supercharged! ## Метаданные - **Канал:** Two Minute Papers - **YouTube:** https://www.youtube.com/watch?v=KAukqp4AdaA - **Дата:** 29.10.2023 - **Длительность:** 6:00 - **Просмотры:** 86,540 ## Описание ❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper "Random-Access Neural Compression of Material Textures" is available here: https://research.nvidia.com/labs/rtr/neural_texture_compression/ My latest paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bret Brizzee, Bryan Learn, B Shang, Christian Ahlin, Gaston Ingaramo, Geronimo Moralez, Gordon Child, Jace O'Brien, Jack Lukic, John Le, Kenneth Davis, Klaus Busse, Kyle Davis, Lukas Biewald, Martin, Matthew Valle, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Richard Sundvall, Steef, Taras Bobrovytsky, Ted Johnson, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's research works: https://cg.tuwien.ac.at/~zsolnai/ #nvidia ## Содержание ### [0:00](https://www.youtube.com/watch?v=KAukqp4AdaA) Intro This paper can simulate really sophisticated virtual material models. Here, billions and billions of numbers describe the reflectance properties of these materials. But, material properties are not the only thing that is important in a virtual world. ### [0:18](https://www.youtube.com/watch?v=KAukqp4AdaA&t=18s) Textures Textures are important too. And with textures, I have good news and bad news for you. Good news: they look fantastic, so much so that you can take a photograph from the real world and add it to your video games. Bad news: they take a ton of data. Gigabytes and gigabytes of data that is often too much for real-time applications and handheld devices. And sometimes, it is too much even for big honking machines and video games. Why? Well, you see in a video game or virtual world, when we get closer to an object, we need to see more detail. Imagine going closer to an open book, and suddenly, a blurry mess emerges. ### [1:04](https://www.youtube.com/watch?v=KAukqp4AdaA&t=64s) Compression What instead should happen is that we should be able to even read that book. So, what does that require? Storing more detail means, yes, you’ve guessed right, Fellow Scholars, that means storing even more data. So, what is the solution? Well, if too much data is the problem, is, of course, the solution is compression. Compression means making things smaller. More compact. We are in luck, because these shiny neural networks are excellent at compression. ### [1:40](https://www.youtube.com/watch?v=KAukqp4AdaA&t=100s) Simulation Compression We just saw how well they have done this job with the material models here. They are also excellent at compressing simulation data. So, scientists at NVIDIA said, let’s do exactly that here too. And now, hold on to your papers Fellow Scholars, and look. This is the previous technique, and this is the new one. For approximately the same amount of data storage, this can provide so much better quality. Wow. That is excellent. But wait, is it excellent enough for real-time applications? I mean, how many seconds does it take to then decompress this and use it in our games? Dear Fellow Scholars, and this is where I fell off the chair when reading this paper. It is not seconds. A thousand times less. Yes. Just approximately one millisecond. That is all it takes to decompress all this information. And you are going to love this one. Do you see this flickering? This is the process of compression by the neural network. It means that it is at work now. The flickering arises as the technique is trying a lot of different, but similar ways to compress it down. Okay, so is this good? Is it doing better over time or worse? Well, look here. Here you see the signal to noise ratio, this is the quality of the reconstruction, the higher the better. And, oh my, I love this. Over time, this is increasing steadily, and don’t forget, the decibel scale is logarithmic, which means a lot of things, but among those lot of things, it means that even a small numerical difference means a big difference in quality. So, how long does this take? Days? Hours? Ah, wait. We are done. The whole process took less than a minute per texture. ### [3:48](https://www.youtube.com/watch?v=KAukqp4AdaA&t=228s) Results That is excellent. So, with that, if we have this beautiful inkwell in a video game, with previous technique, when going closer to it, we saw this, and now, we see this. And all this in real time. That is incredible. Because of the papers, next level games and virtual worlds are coming. But wait, one more final question. This looks good, but what would be the proper, real solution? How much information did we lose during this compression? And the answer is. Almost nothing. Wow. Note that this technique is specifically designed for storing textures, and thus, it also includes compressing different versions of the same image together that we call mip levels and some more magic. And, here is a bombshell result. The previous technique could do this, the new technique can do this. Wow, that book is readable even if we magnify it a great deal. And, my goodness. Look at that! It looks nearly the same as the true solution, and takes about 45 times less data. ### [5:09](https://www.youtube.com/watch?v=KAukqp4AdaA&t=309s) Outro Phew! And do not worry for a second, a bunch of other techniques are also tested against in the paper. Now, not even this one is perfect. For very low bitrates, when we are really short on memory, not so good. Still better than previous techniques, but not ideal. But that is an incredible jump in quality in just one paper. Loving it. What a time to be alive! --- *Источник: https://ekstraktznaniy.ru/video/12955*