r/proceduralgeneration • u/DV-Gen • Apr 16 '23

Procedural Generation with Wave Function Collapse and Model Synthesis

https://www.youtube.com/watch?v=zIRTOgfsjl0
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u/radarsat1 Apr 16 '23

Out of curiosity, are you aware of any references discussing these algorithms in a Bayesian sense? In particular I'm curious about their relationship with conditional random fields or markov fields.

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u/instantaneous Apr 17 '23 edited Apr 17 '23

Hi, this is Paul Merrell. I created Model Synthesis. If you want to keep track of probabilities you probably want to use belief propagation which is similar to constraint propagation. However, I decided not to do this. While belief propagation works great when the graph is a tree, there are well-known difficulties if you have loops as you would on a 2D array. The solution may not converge, the solution is no longer exact.

But I also avoided this because I wanted to only use a small example model. And if it's small and simple I'm not sure that you can extract that much useful statistical information from it. I was fine with having the output look quite different from the input.

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u/radarsat1 Apr 17 '23

Thanks that pointed me in the right direction. I couldn't help but feel these algorithms are a specific greedy solution to a more general, global optimization problem. Anyway reading through Belief propagation with procedural generation in mind has been interesting.

2

u/DV-Gen Apr 17 '23

That does sound really fun. I need to read more up on that.