[xandergos]

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.

[semi-extrinsic]

The paper is cool, and this is not a criticism, but Fig. 4 gives strong "draw the rest of the owl" vibes.

[kelseyfrog]

> in Infinite, Real-Time Terrain Generation

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.

[xandergos]

No worries! It's worded fine as-is given the restrictions. Just wanted to clear up any misunderstandings.

[sieste]

Cool work, congratulations.

Why did you put "real-time" in the title though when generation takes > 7 seconds?

[demarq]

The author points out that the sheer scale of what you generate in that 7 seconds is so vast that you’ll have plenty of time to generate the next tile even when moving at extreme speeds. So your only problem is the first tile, which you can pre compute at the very beginning.

Latency vs Throughput

[treyd]

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.

[noodletheworld]

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.

[xandergos]

Could be! I think maps are definitely the most interesting direction for future research to take.

[unconed]

1) A system that needs _seconds per tile_ is not suitable for real-time anything imo.

The irony is that you explicitly posited your thing as a successor to Perlin noise when in fact, it's just a system that hallucinates detail on top of Perlin (feature) noise. This is dishonest paper bait and the kind of AI hubris that will piss off veterans in the scene.

2) I'm also disappointed that nowhere is there any mention of Rune Johansen's LayerGen which is the pre-AI tech that is the real precedent here.

Every time I see a paper from someone trying to apply AI to classic graphics tech, it seems they haven't done the proper literature study and just cite other AI papers. It seems they also haven't talked to anyone who knows the literature either. https://runevision.com/tech/layerprocgen/

3) >The top level input is perlin noise because it is genuinely the best tool for generating terrain at continental scale

This is a non-sense statement. I don't know what you are thinking here at all, except maybe that you are mistakenly using "Perlin" as a group noun for an entire style of functions.

Perlin has all sorts of well-known issues, from the overall "sameyness" (due to the mandatory zero-crossings and consistent grid size) as well as the vertical symmetry which fails to mimic erosion. Using it as the input to a feature vector isn't going to change that at all.

The idea of using plate tectonics is much better, but, vastly _different_ from what you have done. And btw, every plate tectonics simulation that I've seen does not look convincing. If you treat it as a simple transport problem, the result just looks like a Civilization 1 map. But if you want to treat it seriously, then the tectonics have to be the source of all your elevation changes, and not just some AI hallucination on top afterwards. The features would all have to make sense.

Your abstract states that classic terrains are "fundamentally limited in coherence"... but even to my non-geologist eye, your generated heightmaps seem incredibly blobby and uncanny. This makes me think that a real geologist would immediately spot all sorts of things that don't make any sense. For example, if you added water and rivers to the terrain, would it work, or would you end up with non-sense loops and Escher-like watersheds?

(mostly I'm disappointed that the level of expertise in AI tech is so low that all these things have to be pointed out instead of being things you already knew)

[nathan_douglas]

> And btw, every plate tectonics simulation that I've seen does not look convincing.

It's an amazing problem! I haven't spent much time on it - maybe 20-30 hours spread out over several years - but it _is_ something I come back to from time to time. And it usually ends up with me sitting there, staring at my laptop screen, thinking, "but what if I... no, crap. Or if we... well... no..."

TBH it's one of the things that excites me, because it makes it clear how far we still have to go in terms of figuring out these planet-scale physical processes, simulating them, deriving any meaningful conclusions, etc. Still so much to learn!