It appears that they have developed a novel neural network architecture such that it is possible to understand what each of the layers encode. (Classically, the result of training might not be intelligible--the learned function is encoded in weights and parameters, and aren't meant for human readability or consumption.)
Another interesting property is the "pipeline" and how they seemed to have developed the math to make back propagation work around it. Each step in the pipeline performs some convolution or transformation function.
I haven't read the paper, but I'd be curious to see if they can reuse components of this pipeline in conjunction with one another. Perhaps it wouldn't be immediately possible (I imagine the parameters would have to be adjusted in some shape or form), but a plug-and-play system of pre-trained functions would be nothing sort of amazing.
(I may be incorrect in my analysis. I'm drawing on the ML and image processing I took in undergrad.)
Network learns a system of lighting and geometry, so you can manipulate a set of codes that represent some variables of that geometry and the positions of those lights.
In summary, the most interesting part for the general audience might be the following question -- can we learn a 3D rendering engine just from images or videos without any hand-engineering?
Apart from the interesting applications for computer graphics (like rendering novel viewpoints of an object from various viewpoints), this can also be directly used for vision applications. This is because computer vision can be thought of as the inverse of computer graphics.
Goal of computer graphics: scene description -> images
and
Goal of vision: images -> scene description.
Therefore, training a neural network to behave like a graphics engine is interesting from both these perspectives. We are a LONG way from even scratching the surface.
Another interesting property is the "pipeline" and how they seemed to have developed the math to make back propagation work around it. Each step in the pipeline performs some convolution or transformation function.
I haven't read the paper, but I'd be curious to see if they can reuse components of this pipeline in conjunction with one another. Perhaps it wouldn't be immediately possible (I imagine the parameters would have to be adjusted in some shape or form), but a plug-and-play system of pre-trained functions would be nothing sort of amazing.
(I may be incorrect in my analysis. I'm drawing on the ML and image processing I took in undergrad.)