this post was submitted on 01 Feb 2026
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No Stupid Questions

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I've kinda just had a thought, I don't know if it's horrible or not, so you tell me.

  • 3d terrain in video games is commonly starts with a fractal perlin noise function.
  • This doesn't look very good alone, so some passes are employed to make it look better, such as changing the scale.
  • One such technique is employing hydraulic erosion with a heavy gpu simulation, to create riverbeds and folds in terrain.
  • However hydraulic erosion is VERY slow, and is as such not viable for a game like Minecraft that works in real time. It also doesn't chunk well.

But what if it didn't have to? Why not train something like a diffusion image model off of thousands of pre-rendered high quality simulations, and then have it transform a function like fractal perlin noise? Basically "baking" a terrain pass inside a neural network. This'd still be slow, but slower than simulating thousands of rain droplets? It could easily be deterministic to loop across chunk borders too. You could even train off of real world GIS data.

Has this been tried before?

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[–] hperrin@lemmy.ca 3 points 3 days ago

If it’s not a continuous function, it won’t tile across chunk borders, so we’d have to solve that. That might be solvable by using the current chunks as the outer edges of the image, but I can’t say for sure. Diffusion models don’t usually stay consistent when you do that.