Transcription
Imagine you could hold an entire planet in your hands. A world with mountains that never existed. Coastlines stretching past the horizon in every direction forever. A solo scientist invented something incredible here. One, it is a truly infinite terrain generator. This panel shows a region that is roughly the size of a country like Congo, millions of square miles of land. And if you would zoom in to one of these regions over and over, it just keeps going and going. Amazing.
Now, many of the examples are shown within Minecraft, but it can generate fully continuous worlds depending on the game engine being used. But it gets better, too. This generator also learns more on why that is amazing in a moment. Now, wait, wait, wait. We already have terrain generator programs, a ton of them. So, is this really new?
Well, there are existing methods that build terrain out of noise. These can generate infinitely in every direction, but they aren't that organic. They are a bit too uniform, too repetitive. It just generates new stuff with no plan for the whole planet. No large scale coherence. So, these methods don't really learn. This is the price of speed or there are AI based methods. These can learn. Yes, you can feed them the statistical distribution of real terrain from Earth and have it generate something similar without copying. That is amazing. But unfortunately, they are quite inefficient. Why? Well, because every newly generated area depends on every other area in the world. That's how you get your coherence. It has to know about everything. But it's a nightmare in terms of speed. This takes forever. So learning or speed, choose one. Story of the last 40 years. But this new research essentially fuses these two into one technique that has the advantages of both. Is that even possible? After 40 years of using noise to create virtual worlds, does anyone think there is a better way? Let's have a look.
Dear fellow scholars, this is two minute papers with Dr. Koa Eher. This key formula is brilliant. First, it uses diffusion to create these terrains. So much like the image generator AI systems of today, it starts out from noise and it slowly reorganizes it to an image. But this concept is now adapted to terrains. Okay, that is a cool building block, but that is not new.
Now, here comes the brilliance one. This formula tells us what a new region R should look like. It says, well, ask a bunch of overlapping windows that touch region R, run the noising on each and take their weighted average. In other words, blur together the opinions of the neighbors who can see this path and ignore everyone else. Now, this this is genius. Why? Well, by asking only the neighbors, it decouples the cost of the query from the size of the world. In simpler words, as the world you generate grows, the technique does not get slower. H. Now, that is an amazing property. For instance, it lets you teleport millions of miles instantly. How cool is that? Love it.
But we have a problem. The problem is that real terrain can span huge height differences like an ocean trench to Mount Everest. Huge variation. However, the part that really makes terrain look like terrain is only a few feet tall. Ridges, river banks, and all kinds of textures. Diffusion techniques can't deal with that. They either focus on the small terrain variations or large mountains, but not the two at the same time. So what do we do now?
Genius idea number two. This is called the llation reextraction denoising for height maps. The trick says do not den noiseise the heights as a raw signal. No sir get this. Imagine photographing your friend standing at the foot of the mountain. H if you frame the mountain in the shot the person becomes a tiny little dot. Well then of course focus on the person then. Well, if you do that, then you don't see any of the mountain. So, what do we do? Well, here is the brilliance of the laplation trick. Are you ready? Okay. So, first you take an image of the mountain, a perfect image. Then you take a separate photo of the person on their own scale so you see them properly. And then you put the two together onto one photo. Yep. Now you retain all the detail about both. So good. And this is what this tab does mathematically. And that is also how this technique generates terrain on multiple scales. So finally the mountains and the creeks have an even fight. So finally an efficient technique that can also learn about Earth or any other kind of data and generate new planets.
And get this, as I keep reading the paper, it just gets better and better. Now, hold on to your papers, fellow scholars, because this was trained in two weeks and was run on a 4-year-old consumer GPU interactively. The code and Minecraft mod are available for free. The power of open science. What a time to be alive. And this was written by a solo scientist. The paper was published at Sigraph, the most prestigious conference in computer graphics. This is incredible. This is a bit like showing up at the Olympics alone without a team and winning a gold medal. Yep, an independent scientist. That is an amazing achievement. And he just gives it all away to all of us for free. Thank you so much and huge congratulations.
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