They are tough, AI isn’t building a logical model of a human when drawing them. It’s more like taking a best guess where pixels should go. So it’s not “thinking”: Alright, drawing a human, human has two hands, each hand has five fingers, the fingers are posed like this, …
It’s drawing a human, so it roughly throws a human shape on there, human shape roughly has a head, when there is a torso two arms should come out (roughly) and on the end of those two arms is something too, but what that is is complicated and always looks different. It’s all approximation, extremely well done, but in the end the AI is just guessing where to put something.
If you trained a model on just a single type of hand and finger position it would perfectly replicate it. But every hand is different and each hand has a near unlimited amount of positions it can be in (including each finger). So it’s usually a mess.
I saw one way to get better results, but that’s pretty much giving the AI beforehand a pose (like a stick figure) so it already knows where things should go. If you just freely generate “Human male, holding hands up” you probably get a mess with 6 fingers out and maybe a third arm going to nowhere in the back.
The rest of your answer makes sense but this rhetorical question is not helpful IMO. There are lots of things that humans are not good at but at which computers excel.
That’s mostly true, but not fully. Models use human drawn images and photos to learn from. So if you put in millions of drawn images and the hands aren’t perfect in all of them, you might mess up the model too. That’s why negative prompts like “malformed”, “bad quality”, “misformed hands” and so on are popular when playing with image generation.
How? Humans are not good at finding the square root of numbers but computers are much better at it. Human limitations are not relevant in cases like this.
Why are humans so bad with drawing hands?
They are tough, AI isn’t building a logical model of a human when drawing them. It’s more like taking a best guess where pixels should go. So it’s not “thinking”: Alright, drawing a human, human has two hands, each hand has five fingers, the fingers are posed like this, …
It’s drawing a human, so it roughly throws a human shape on there, human shape roughly has a head, when there is a torso two arms should come out (roughly) and on the end of those two arms is something too, but what that is is complicated and always looks different. It’s all approximation, extremely well done, but in the end the AI is just guessing where to put something.
If you trained a model on just a single type of hand and finger position it would perfectly replicate it. But every hand is different and each hand has a near unlimited amount of positions it can be in (including each finger). So it’s usually a mess.
I saw one way to get better results, but that’s pretty much giving the AI beforehand a pose (like a stick figure) so it already knows where things should go. If you just freely generate “Human male, holding hands up” you probably get a mess with 6 fingers out and maybe a third arm going to nowhere in the back.
The rest of your answer makes sense but this rhetorical question is not helpful IMO. There are lots of things that humans are not good at but at which computers excel.
That’s mostly true, but not fully. Models use human drawn images and photos to learn from. So if you put in millions of drawn images and the hands aren’t perfect in all of them, you might mess up the model too. That’s why negative prompts like “malformed”, “bad quality”, “misformed hands” and so on are popular when playing with image generation.
Wait. I thought he was perfect with that analogy.
How? Humans are not good at finding the square root of numbers but computers are much better at it. Human limitations are not relevant in cases like this.
We’re not talking about square roots of numbers though, we’re talking about drawing hands.
This happens to be one of the cases that humans and AI both struggle with, because drawing hands is complicated for both entities.
Yes but you can train ML models on photographs of hands to bypass that limitation?
Of course. Just like you can train humans to bypass their limitations.
The problem is training. There’s nothing intrinsic to AI art that prevents it from making perfect hands. It just takes time, and a lot of data.
Is there a dearth of any of these?