Its outputs can provide the inputs for NeRF training, which is why they mention NeRFs. But it's not NeRF technology.
From the SJC paper:
> We introduce a method that converts a pretrained 2D diffusion generative model on images into a 3D generative model of radiance fields, without requiring access to any 3D data. The key insight is to interpret diffusion models as function f with parameters θ, i.e., x = f (θ). Applying the chain rule through the Jacobian ∂x/∂θ converts a gradient on image x into a gradient on the parameter θ.
> Our method uses differentiable rendering to aggregate 2D image gradients over multiple viewpoints into a 3D asset gradient, and lifts a generative model from 2D to 3D. We parameterize a 3D asset θ as a radiance field stored on voxels and choose f to be the volume rendering function.
Interpretation: they take multiple input views, then optimize parameters (a voxel grid in this case) to a differentiable renderer (the volume rendering function for voxels) such that they can reproduce the input views.
[0]: https://pals.ttic.edu/p/score-jacobian-chaining [1]: https://github.com/awesome-NeRF/awesome-NeRF
It's definitely a fun time to be involved.