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by desdenova
613 days ago
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I think the closest we have right now is 3D gaussian splatting. So far it's only been used to train a scene from photographs from multiple angles and rebuild it volumetrically by adjusting densities in a point-cloud. But it might be possible to train a model on multiple different scenes, and perform diffusion on a random point cloud to generate new scenes. Rendering a point cloud in real time is also very efficient, so it could be used to create insanely realistic game worlds instead of polygonal geometry. It seems someone already thought of that: https://ar5iv.labs.arxiv.org/html/2311.11221 |
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I was suggesting a more modest approach, I guess, one where the reverse-denoising process involves picking and placing existing 3D assets, e.g., those in GTA 5, so that the process is actually building a plausible map, using those 3D assets, but on the fly...
Turn your car right and a plausible street decorated with buildings, trees and people is dreamt up by the algorithm. All the lighting and physics would still be done in-engine, with stable diffusion acting as a dynamic map creator, with an inherent knowledge of how to decorate a street with a plausible mix of assets.
I suppose it could form the basis of a procedurally generated game world where, given the same random seed, it could generate whole cities or landscapes that would be the same on each player's machine. Just an idea...