> So, if traditional game worlds are paintings, neural worlds are photographs. Information flows from sensor to screen without passing through human hands.
I don't get this analogy at all. Instead of a human information flows through a neural network which alters the information.
> Every lifelike detail in the final world is only there because my phone recorded it.
I might be wrong here but I don't think this is true. It might also be there because the network inferred that it is there based on previous data.
Imo this just takes the human out of a artistic process - creating video game worlds and I'm not sure if this is worth archiving.
Its a time capsule, among other things. I want to take many, many videos of my grandpa's farm, and be able to walk around in it in VR using something like this in the future.
It would be quite interesting to try to mess with the neural representations do add or remove the images of some objects there. I'm also curious if the topology of the actual place is similar to the topology of the embedding space.
This might be a vague question, but what kind of intuition or knowledge do you need to work with these kind of things, say if you want to make your own model? Is it just having experience with image generation and trying to incorporate relevant inputs that you would expect in a 3D world, like the control information you added for instance?
I think this is very interesting because you seem to have reinvented NeRF, if I’m understanding it correctly. I only did one pass through but it looks at first glance like a different approach entirely.
More interesting is that you made an easy to use environment authoring tool that (I haven’t tried it yet) seems really slick.
Both of those are impressive alone but together that’s very exciting.
I love this! Your results seem comparable to the counter strike or minecraft models from a bit ago with massively less compute and data. It's particularly cool that it uses real world data. I've been wanting to do something like this for a while, like capturing a large dataset while backpacking in the cascades :)
I didn't see it in an obvious place on your github, do you have any plans to open source the training code?
This is very impressive for a hobby project. I was wondering if you were planning to release the source code. Being able to create client-hosted, low-requirement neural networks for world generation could be really useful for game dev or artistic projects.
Appreciate this article that shows some failures on the way to a great result. Too many times, people only show how the polished end-result: look, I trained this AI and it produces these great results. The world dissolving was very interesting to see, even if I'm not sure I understand how it got fixed.
Is this a solo/personal project? If it is is indeed very cool.
Is OP the blog’s author? Because in the post the author said that the purpose of the project is to show why NN are truly special and I wanted a more articulate view of why he/she thinks that?
Good work anyway!
Really cool. How much compute did you require to successfully train these models? Is it in the ballpark of something you could do with a single gaming GPU? Or did you spin up something fancier?
edit: I see now that you mention a pricepoint of 100 GPU-hours/roughly 100$. My mistake.
World Emulation via Neural Network
(madebyoll.in)207 points by treesciencebot 17 hours ago | 37 comments
Comments
I don't get this analogy at all. Instead of a human information flows through a neural network which alters the information.
> Every lifelike detail in the final world is only there because my phone recorded it.
I might be wrong here but I don't think this is true. It might also be there because the network inferred that it is there based on previous data.
Imo this just takes the human out of a artistic process - creating video game worlds and I'm not sure if this is worth archiving.
https://oasis.decart.ai/
More interesting is that you made an easy to use environment authoring tool that (I haven’t tried it yet) seems really slick.
Both of those are impressive alone but together that’s very exciting.
Link to the demo in case people miss it [1]
> using a customized camera app which also recorded my phone’s motion
Using phone's gyro as a proxy for "controls" is very clever
[1] https://madebyoll.in/posts/world_emulation_via_dnn/demo/
I didn't see it in an obvious place on your github, do you have any plans to open source the training code?
What could go wrong?
Jokes aside, this is insanely cool!
Imagine a similar technique but with productivity software.
And a pre-trained network that adapts quickly.
Is OP the blog’s author? Because in the post the author said that the purpose of the project is to show why NN are truly special and I wanted a more articulate view of why he/she thinks that? Good work anyway!
edit: I see now that you mention a pricepoint of 100 GPU-hours/roughly 100$. My mistake.
https://en.m.wikipedia.org/wiki/LSD:_Dream_Emulator