  Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant
challenge that existing approaches struggle to address without extensive
modifications to vanilla NeRF framework. We introduce InsertNeRF, a method for
INStilling gEneRalizabiliTy into NeRF. By utilizing multiple plug-and-play
HyperNet modules, InsertNeRF dynamically tailors NeRF's weights to specific
reference scenes, transforming multi-scale sampling-aware features into
scene-specific representations. This novel design allows for more accurate and
efficient representations of complex appearances and geometries. Experiments
show that this method not only achieves superior generalization performance but
also provides a flexible pathway for integration with other NeRF-like systems,
even in sparse input settings. Code will be available
https://github.com/bbbbby-99/InsertNeRF.
