Rendering a scene is a function that takes lights, camera, geometry, textures as inputs and outputs an image.

This function is very complex and hard to invert. But, and this is the interesting thing I got from the article, it is similar enough to deep learning functions that the same strategy works: We can take gradients of it and optimize the inputs to fit the target output.

So, while we can't compute the 3D model that would generate the target picture directly. We can ask it "How do we nudge the model to make it more similar to what we want?". If we keep nudging it, eventually it will be very similar. The tool will find the exact position of the lights, the textures, model, etc.