3 ms·

Apparently, he published all of his code (I didn't see any mention of where). Regarding the color -- he mentions being careful with the palette, but I wonder ho

Apparently, he published all of his code (I didn't see any mention of where). Regarding the color -- he mentions being careful with the palette, but I wonder how he gets it to turn out correctly (assuming it's RNG). It seems like you would have to keep generating new instances until you find one that looks pleasing. One algorithm that comes to mind: 1. Choose a color palette and initial resolution. 2. Implement generative features at current resolution. 3. Generate the output and until it "looks right". 4. Increase feature resolution. 5. Go to step 2. In other words, one could keep generating prints until the locations and extents of the sampled colors "looks right", then keep the random seed and continue implementing finer details until they "look right", etc. This will be the second time I drop this on HN this week, but check out Primitive Pictures[0] if you are interested in this kind of stuff. Also, I highly recommend Schiffman's YouTube channel[1] which goes into depth on how to get started with processing (and p5.js, the browser/canvas version). Note that the artist in the article learned how to do this by reading Schiffman's book, which is covered in a series of tutorials in the linked YouTube channel. [0] https://github.com/fogleman/primitive https://github.com/fogleman/primitive [1] https://www.youtube.com/channel/UCvjgXvBlbQiydffZU7m1_aw https://www.youtube.com/channel/UCvjgXvBlbQiydffZU7m1_aw

machinelearning 8y ago
It seems to be published here: https://github.com/manoloide/AllSketchs https://github.com/manoloide/AllSketchs
mkl 8y ago
I don't think this algorithm is very common. In particular, the colour palette can usually be chosen last, and a lot of generative art (at least all that shown in the article) will not need multiple resolution passes. I think a more common discovery/refinement process is to go from more random to less random, or random in particular kinds of ways. For example, you can specify the values or distributions (or at least ranges of values) for particular parameters based on things you think will look good or which should end up similar in ways you like to things you've found already. Generative art is a search in an extremely high dimensional space. The more you go into it aiming for something in particular, the more nailed down it is and the closer to traditional art (and the harder to pull off well IMHO, think how much difference slightly changing the position of one of the elements in these images would make, versus slightly changing the position of someone's eye). If you go looking for "new and interesting" or "looks good" without really aiming for anything in particular, it can be a lot easier to find good things. I've seen a couple of really interesting talks recently about the surprising successfulness of objective-less search by Ken Stanley: https://www.youtube.com/watch?v=dXQPL9GooyI https://www.youtube.com/watch?v=dXQPL9GooyI (40 min.) https://www.youtube.com/watch?v=tZBViI8ZaU0 https://www.youtube.com/watch?v=tZBViI8ZaU0 (90 min.) He started off looking at human-directed machine-evolved generative art, and ended up with a far more general discovery.

machinelearning 8y ago

It seems to be published here: https://github.com/manoloide/AllSketchs https://github.com/manoloide/AllSketchs

mkl 8y ago

I don't think this algorithm is very common. In particular, the colour palette can usually be chosen last, and a lot of generative art (at least all that shown in the article) will not need multiple resolution passes. I think a more common discovery/refinement process is to go from more random to less random, or random in particular kinds of ways. For example, you can specify the values or distributions (or at least ranges of values) for particular parameters based on things you think will look good or which should end up similar in ways you like to things you've found already. Generative art is a search in an extremely high dimensional space. The more you go into it aiming for something in particular, the more nailed down it is and the closer to traditional art (and the harder to pull off well IMHO, think how much difference slightly changing the position of one of the elements in these images would make, versus slightly changing the position of someone's eye). If you go looking for "new and interesting" or "looks good" without really aiming for anything in particular, it can be a lot easier to find good things. I've seen a couple of really interesting talks recently about the surprising successfulness of objective-less search by Ken Stanley: https://www.youtube.com/watch?v=dXQPL9GooyI https://www.youtube.com/watch?v=dXQPL9GooyI (40 min.) https://www.youtube.com/watch?v=tZBViI8ZaU0 https://www.youtube.com/watch?v=tZBViI8ZaU0 (90 min.) He started off looking at human-directed machine-evolved generative art, and ended up with a far more general discovery.