You must've gone to a special math school or smth) I learned about them in the uni and as with 90% of material it was quite dry and formal. Pretty sure they don't talk about generative art while teaching them in 2022 (and they probably should:).
For MarkovJunior, the recent projects that were impactful the most were Imagegram by Guilherme S. Tows [1] and Daniel Ritchie's dissertation [2] about PPLs for procgen. I took quite a different approach from Ritchie's though.
That thesis is really fun, but it will take me a couple weeks to digest. Before it even starts I see Pat Hanrahan, who is one of the nicest most creative people I have met in CS (), I know this going to be good.
A fantasy of mine is to have a bag of arbitrary constraints and behaviors of agents that exercise the system. One could sketch a building, model the behavior of people that will use it and let the system run, doing backwards and forwards inference to evolve a structure that makes those agents satisfied across lots of criteria. The designer if they are still called that, can select designs they like and the system can use that as a seed or test oracle. Virtual cows, cow paths and evolvable structures wrt those cow paths.
What do you think of "Growing Neural Cellular Automata" [1]
There are special approaches to generating music. The best for ratio of quality/complexity that I know of are Markov constraints https://www.youtube.com/watch?v=buXqNqBFd6E and WaveNet. I don't think WFC offers something useful and new for generation of music.
Yes, (C1) is a constraint problem. But we also want to satisfy (C2) as close as possible, otherwise we could have just colored some outputs in a single color.
Yes, I took that to mean, when you're making a random valid choice, the weights come from the input distribution.
I don't understand about "a single color" unless you mean setting all output pixels to the same color, which would only satisfy the constraints if the input has an NxN patch all one color.
I hope my comment didn't seem to imply that the work seemed unoriginal. I don't think that; I wanted to check my reading.
Thank you for writing this (and your last comment) out explicitly. The quantum mechanics description confused me (and possibly other from reading the thread here). Although I can understand how that inspired this work in the first place.
The algorithm used is exactly what you described here. It wasn't obvious to me that probability density functions were not tracked (the algorithm only tracks which NxN patch are allowed at each location) and randomness only come into play when a random valid choice is made, and there each valid patch is chosen probability proportional to its number of occurrence in the input.
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.
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!
We need to interpret those coefficients somehow. Real coefficients can be interpreted as mixing of colors, but for complex ones I don't see a good interpretation.
I'm not experienced with the license law, but people told me that it's better to have license text in source files themselves, because I have samples in the repo that I have no idea who has rights for.
I wonder too =). But it'll run like forever on a high res image. For high res image you want to use something like texture synthesis, see my reply to fitzwatermellow for more.
Yeah, you a right, I'll upload slower gifs. Right now youtube video has the slowest speed, in fact it has segments with no frame-skipping at all: https://youtu.be/DOQTr2Xmlz0
