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Show HN: Wave function collapse algorithm

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Re: Show HN: Wave function collapse algorithm

#121

Post by ExUtumno »

abecedarius wrote: post #117
ExUtumno wrote: post #116

Earlier quoted context omitted.

Most of the examples in the repo have those NxN all one color patches. Or, without (C2) the algorithm would have generated completely empty integrated circuits, or completely grass terrain, which is really boring. You understood right, it's constraints + probabilities. Btw, I have different algorithm that satisfies (C2) perfectly, but not (C1): https://github.com/mxgmn/ConvChain

Thanks! I might try this approach to generate formal poetry -- it's something I've done by backtracking before, and I'd considered doing something like your ConvChain.

At first I thought that my methods don't offer anything new to text generation besides the Markov chain, but several people already proposed ideas that sound sensible, so let me know if you make anything!

Re: Show HN: Wave function collapse algorithm

#122

Post by HelloNurse »

derefr wrote: post #72
ExUtumno wrote: post #35

Earlier quoted context omitted.

Thanks! Efros' and Leung's method doesn't satisfy the (C1) condition. The closest previous work is Paul Merrel's model synthesis. WFC and texture synthesis serve similar purposes: they produce images similar to the input image. However, the definition of what is "similar" is different in each case. If you have a high def input with noise (like realistic rocks and clouds) then you really want to to use texture synthes…

> something like the inner rules of that image and long range correlations I assume that if you feed WFC a large input image, it just thinks of that as a very complex set of rules that are harder to satisfy than those of a small input? Is there a way, then, to instead train the WFC algorithm on a large corpus of small, similar samples, such that it can try to derive the rules common to all the inputs in the corpus, a…

>I assume that if you feed WFC a large input image, it just thinks of that as a very complex set of rules that are harder to satisfy than those of a small input?

Since the input is shredded into a multiset of N by M rectangles, it's the opposite: assuming the small input image is a portion of the large one, the large image model adds examples to the ones in the small image model, so the set of cases it can fit an example to is the same or larger.

Re: Show HN: Wave function collapse algorithm

#123

Post by MikeTV »

ExUtumno wrote: post #38
vinchuco wrote: post #20

Can you feed it something other than bitmaps? Like its own source code?

Source code is a 1-dimensional array. For 1-dimensional arrays WFC is just a Markov chain. 2 and higher dimensional arrays are much more interesting because they have cycles, and there is no canonical way to generalize Markov chains to higher dimensions.

What about code in a bitmap-based language like Piet?

https://en.wikipedia.org/wiki/Esoteric_programming_language#...

Re: Show HN: Wave function collapse algorithm

#126

Post by mathgenius »

ExUtumno wrote: post #111
mathgenius wrote: post #100

Earlier quoted context omitted.

I wonder if it would be interesting to purposely search for tilesets that maximize contradiction rate. What would those things look like?

A very good question! The opposite of it is also important, can we follow some heuristics while creating tilesets to minimize contradiction rates, but not making tilesets easy? I don't know. If someone knows please tell me.

Hmm, this possibly relates to sheaf theory. Robert Ghrist has a good book about this stuff (applied topology) if you want to check it out.

Re: Show HN: Wave function collapse algorithm

#127

Post by Aqwis »

ExUtumno wrote: post #35
fitzwatermellow wrote: post #4

Great work! The fact that it captures "long-range order" seemingly perfectly is something not many have been able to do before! And the "collapse" visualization is great fun to watch. But is your algorithm really qualitatively all that different from previous search methods (e.g. Efros and Leung), if you are still (uniform random?) sampling over the input distribution of patches? I notice also your input textures ten…

Thanks! Efros' and Leung's method doesn't satisfy the (C1) condition. The closest previous work is Paul Merrel's model synthesis. WFC and texture synthesis serve similar purposes: they produce images similar to the input image. However, the definition of what is "similar" is different in each case. If you have a high def input with noise (like realistic rocks and clouds) then you really want to to use texture synthes…

Didn't know where else to contact you, but I made a straight [Java port](https://github.com/Aqwis/JavaWaveFunctionCollapse) just as a fun exercise. Feel free to add it to your README.md if you want.
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