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Generalizing Neural Radiance Fields (NeRF) to new scenes is a significantchallenge that existing approaches struggle to address without extensivemodifications to vanilla NeRF framework. We introduce InsertNeRF, a method forINStilling gEneRalizabiliTy into NeRF. By utilizing multiple plug-and-playHyperNet modules, InsertNeRF dynamically tailors NeRF's weights to specificreference scenes, transforming multi-scale sampling-aware features intoscene-specific representations. This novel design allows for more accurate andefficient representations of complex appearances and geometries. Experimentsshow that this method not only achieves superior generalization performance butalso provides a flexible pathway for integration with other NeRF-like systems,even in sparse input settings. Code will be availablehttps://github.com/y-99/InsertNeRF.

InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules