> The code is being prepared for public release; pretrained weights and full training/inference pipelines are planned.
Any ideas of how it would different and better compared to "traditional" PCG? Seems like it'd give you more resource consumption, worse results and less control, neither of which seem like a benefit.
This could be a great way to make backrooms horror environments!
I've dreamed of a NeRF-powered backrooms walking simulator for quite a while now. This approach is "worse" because the mesh seems explicit rather than just the world becoming what you look at, but that's arguably better for real-world use cases of course.
I wonder if they also have a strategy for deleting generate tiles, otherwise the infinite is limited to the size of available memory. I also wonder if with their method can exactly recreate tiles that have been deleted. Or in other words, that they have a method for generating unique seeds for all tiles. The paper does not give much technical details. If the seed has a limited size and there is a method for generating seeds for each 2D coordinate, I wonder if it is possible to make a non-repeating infinite world. I think it is not possible with a limited size seed.
Games have used procedural world generation since at least the 1980s, and on 8-bit home computers. Glancing through the video and webpage, the results don't look much different from what's possible with traditional Wave Function Collapse tbh.
Is it just me, or some of the places it generates are just not realistic? Like a small area of some kind which is a dead space, and there is a giant window into it.
Yeah, I think either the method doesn't work well, or there is something off with their tuning.
Their block-by-block generation method seems to be too local in its considerations, where each 3x3 section (= the ones generated based on the immediate neighbors) looks a lot more coherent than the 4x4 sections and above. I think it might need to be extended to be less local and might also in general need to be paired with some sort of guidance systems (e.g. in the office example would generate the overall floor layout).
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[ 2.8 ms ] story [ 41.6 ms ] thread> The code is being prepared for public release; pretrained weights and full training/inference pipelines are planned.
Any ideas of how it would different and better compared to "traditional" PCG? Seems like it'd give you more resource consumption, worse results and less control, neither of which seem like a benefit.
It's about generating interesting virtual space!
[1] https://www.challies.com/articles/no-mans-sky-and-10000-bowl...
I've dreamed of a NeRF-powered backrooms walking simulator for quite a while now. This approach is "worse" because the mesh seems explicit rather than just the world becoming what you look at, but that's arguably better for real-world use cases of course.
Their block-by-block generation method seems to be too local in its considerations, where each 3x3 section (= the ones generated based on the immediate neighbors) looks a lot more coherent than the 4x4 sections and above. I think it might need to be extended to be less local and might also in general need to be paired with some sort of guidance systems (e.g. in the office example would generate the overall floor layout).