I get the feeling that some abstract geometric/topological approaches will let us get under fitted general pretrained NERFs that rapidly specialize to a given c

I get the feeling that some abstract geometric/topological approaches will let us get under fitted general pretrained NERFs that rapidly specialize to a given context, let you combine separate contexts into a composite scene, and maybe let's you give behaviors to entities in the scene. At that point you're kind of trying to generically train over all possible scene graphs. But maybe some kind of geometric delocalization scheme lets you train on small fragments of larger scenes, transfer learning between scenes, and quickly represent and render generic scenes.