[HN Gopher] Terrain Diffusion: A Diffusion-Based Successor to Pe...
___________________________________________________________________
Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise
Author : kelseyfrog
Score : 76 points
Date : 2025-12-10 18:37 UTC (4 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| vessenes wrote:
| 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.
| strongbond wrote:
| No it's not. The whole presentation of it is confusing. Sorry.
| Imnimo wrote:
| 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.
| euleriancon wrote:
| In practice you can use 2d generation on spheres with simple UV
| mapping techniques. Your pixel height becomes distance from the
| sphere origin.
| lawlessone wrote:
| 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.
| lawlessone wrote:
| It's interesting but i can't see it replacing Perlin or Simplex
| noise which are used for more than just terrain generation.
|
| edit: I don't think i have the vocabulary to describe other
| issues i have other than it doesn't feel like the right way to
| "solve" this problem.
|
| I'd prefer something that was entirely code rather than requiring
| training, and possibly retraining to get what i want.
|
| edit2: Also is this entirely flat ? or can it be applied to a
| sphere (planet) , or terrain inside a cylinder (rotating space
| habitat) ?
| euleriancon wrote:
| 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.
| reactordev wrote:
| 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.
| orbital-decay wrote:
| 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.
| noodletheworld wrote:
| 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.
| wafngar wrote:
| Very common in ML research these days - claim novelty / cite
| prior work in an obfuscated way and so on.
___________________________________________________________________
(page generated 2025-12-10 23:00 UTC)