I worked on something very similar for my master's degree.
The problem I could never solve was the speed, and from reading the paper it doesn't seem like they managed to solve that either.
In the end, for my work, and I expect for this work, it is only usable for pre generated terrains and in that case you are up against very mature ecosystems with a lot of tooling to manipulate and control terrain generation.
It'll be interesting to see of the authors follow up this paper with research into even stronger ability to condition and control terrain outputs.
I came here to say this. My masters was on procedural generation. Perlin, fBm, etc. The things these noise functions have that an LLM doesn’t is speed. 1-D perlin is just a dozen or so multiplications with a couple random coefficients. The GPU can do 4-D Perlin all day long every frame taking up a 4096x4096x32 texture volume.
While I do like the erosion effects and all, having a few height texture brushes that have those features that you can multiply on the GPU is trivial. I still welcome these new approaches but like you said, it’s best for pre generation.
My masters was also on procedural generation. Now I wonder how many of us are out there.
At any rate, given that this paper divides the terrain in regions and apparently seeds each region deterministically, it looks like one could implement a look-ahead that spawns the generation on async compute in Vulkan and lets it cook as the camera flies about.
Which is what a sane terrain system would do. Just beyond the far plane you would load/gen the tile/chunk and as you got closer, increase the resolution/tessalation/etc. (or you start with high and each level away you skip vertices with a wider index march for a lower lod).
In any case, like I said, I welcome any new advances in this area. Overhang being the biggest issue with procedural gen quad terrain. Voxel doesn’t have that issue but then suffers from lack of fine detail.
I think it's catnip for programmers, myself included. (See also: boids, path traced renderers, fluid simulations, old fashioned "generative"/plotter art, etc. - stuff with cool visual output)
Hi everyone, I wrote this paper. Cool to see it has been posted here already.
I want to clarify some points on things other people have mentioned:
- This architecture is not as fast as Perlin noise. IMO it is unlikely we will see any significant improvement on Perlin noise without a significant increase in compute, at least for most applications. Nonetheless, this system is not too slow for real-time use. In the Minecraft integration, for instance, the bottleneck in generation speed is by far Minecraft's own generation logic (on one RTX 3090 Ti).
- I agree that this is not "production-ready" for most tasks. The main issue is that (1) terrain is generated at realistic scales, which are too big for most applications, and (2) the only control the user has is the initial elevation map, which is very coarse. Thankfully, I expect both of these issues to be fixed pretty quickly. (1) is more specific to terrain generation, but I have a number of ideas on how to fix it. (2) is mostly an issue simply because I did not have the time to engineer a system with this many features (and as-is, the system is quite dense). I believe a lot of existing work on diffusion conditioning could be adapted here.
- The post title misses one key part of the paper title: "in Infinite, Real-Time Terrain Generation." I don't expect this to replace perlin noise in other applications. And for bounded generation, manual workflows are still superior.
- The top level input is perlin noise because it is genuinely the best tool for generating terrain at continental scale. If I had more time on my hands, I would like to use some sort of plate tectonics simulator to generate that layout, but for something simple, reasonably realistic, and infinite, perlin noise is pretty much unbeatable. Even learned methods perform on-par with perlin noise at this scale because the data is so simple.
I think it's pretty neat that, in implementing this for the game, you wrote a terrain gen mod lets you call out to a server running as a separate process. I can't believe nobody had the thought of doing that before (to my knowledge), but it makes so much sense.
My sincerest apologies. The submission disallowed the title in its entirety. It's generally unclear if the guidance for submitters favors omission or rewording. I take full responsibility for omitting those qualifiers.
The irony of this paper is that the part that isn't geographic (towns, roads, fields, etc) which have non-random ordered structure are the parts that are most suitable for this approach.
Convincing AND useful procedural terrain is usually hard-simulated along some manually placed guides, which is typically faster and more versatile than a diffusion model. I don't see any model being used in practice for this, at least not until it has good controlnets trained specifically for this task. However something like this can be useful for texture generation, especially with geometry/camera position/lighting as additional inputs.
Not just this could improve Minecraft world generation (heh) but this could also have good use on 3D surface material generation as well, namely on the layering of different materials and generation using multi diffusions, if you look at the surface as a microscopic terrain
This is really awesome, actually! It looks great, very diverse, and clearly is scalable to extremely large maps. Props for testing the generation out on Minecraft - where terrain generation really matters.
I wonder what it would take to adapt a model like this to generate non-Earthlike terrain. For example, if you were using it to make planets without atmospheres and without water cycles, or planets like Io with rampant volcanism.
Since 1996, Ken Perlin has published a whole bunch of extremely cool Java applet demos on his web page, which he uses to teach his students at NYU and anyone who wanted to learn Java and computer graphics. One of his demos was a procedural planet generator!
I learned a lot from his papers and demo code, and based the design of The Sims character animation system on his Improv project.
Will it not get all bunched up near the poles though? and maybe have seam where the ends of the tiles meet?
edit: Perlin noise and similar noise functions can be sampled in 3d which sorta fixes the issues i mention , and higher dimensions but i am not sure how that would be used.
I've thought about this before, and I think there is some way you could find to do it. For example, you could generate on the mercator projection of the world, and then un-project. But the mercator distorts horizontal length approaching the poles. I think it would be complex to implement, but you could use larger windows closer to the poles to negate this.
Mm. This paper makes it hard to understand what they've done.
For example:
> MultiDiffusion remains confined to bounded domains: all windows must lie within a fixed finite canvas, limiting its applicability to unbounded worlds or continuously streamed environments.
> We introduce InfiniteDiffusion, an extension of MultiDiffusion that lifts this constraint. By reformulating the sampling process to operate over an effectively infinite domain, InfiniteDiffusion supports seamless, consistent generation at scale.
…but:
> The hierarchy begins with a coarse planetary model, which generates the basic structure of the world from a rough, procedural or user-provided layout. The next stage is the core latent diffusion model, which transforms that structure into realistic 46km tiles in latent space. Finally, a consistency decoder expands these latents into a high-fidelity elevation map.
So, the novel thing here is slightly better seemless diffusion image gen.
…but, we generate using a heirsrchy based on a procedural layout.
So basocally, tldr: take perlin noise, resize it, and then image-2-image use it as a seed to generate detailed tiles?
People have already been doing this.
Its not novel.
The novel part here is making the detailed tiles slightly nicer.
Eh. :shrug:
The paper obfuscates this, quite annoyingly.
Its unclear to me why you cant just use multi diffusion for this, given your top level input is already bounded (eg. User input) and not infinite.
The problem I could never solve was the speed, and from reading the paper it doesn't seem like they managed to solve that either.
In the end, for my work, and I expect for this work, it is only usable for pre generated terrains and in that case you are up against very mature ecosystems with a lot of tooling to manipulate and control terrain generation.
It'll be interesting to see of the authors follow up this paper with research into even stronger ability to condition and control terrain outputs.
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