[pvillano]

Neural proxies are a constant-time approximator for anything.

There's still difficulty in finding exactly where the proxy should go, i.e. what step can be approximated without losing fidelity in the output. Apparently, you also need to select auxiliary features to guide training. But if you figure those out, you can replace hours of computation with milliseconds, accurate to the limits of human perception.

[genxy]

Aren't they a faster, lower fidelity model, like all models?

[pvillano]

It is a faster, lower fidelity model.

but it's not JUST a faster, lower fidelity model.

The new model can be queried in new ways. The encoding from the RenderMan scene into the neural proxy allows for differentiable relighting. Once you have differentiability, you can find out what inputs create a desired output with gradient descent, instead of trying every possible input.

Neural proxies are also much, much faster for only a small drop in fidelity. 1 hour to 60hz is a 216,000x speedup. That's not possible without neural proxies. You could try lowering precision, resolution, bounces, scene complexity. Accuracy would be out the window before you get close to that performance.

[genxy]

https://www.eigensteve.com/

https://www.youtube.com/@Eigensteve/search?query=pinn