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well actually image output is fixed and there s lots of training data. Neural networks can learn anything in their latent space so there is no need to impose 3D
by seydor 1y ago
well actually image output is fixed and there s lots of training data. Neural networks can learn anything in their latent space so there is no need to impose 3D rendering constraints, and it s not evident that it's less efficient (for the model).
3D model rendering would be useful however for interfacing with robots.
- teamonkey 1y agoYou often view 3D games on a 2D screen. That doesn’t mean that a game is natively 2D and the 3D world is an inconvenient step that can be bypassed. Actually the opposite, the 2D representation on screen is just a projection. In VR, for example, the same 3D scene will be rendered twice, once for each eye, from two viewpoints 10-15cm apart. If you don’t have an internal 3D representation of the world, the AI would need to generate exactly the same scene from a very slightly different perspective for each eye, without any discrepancies or artefacts. And that’s not even discussing physics, collisions or any form of consistent world logic that happens off-screen. Or multiplayer!