  We present a method for automatically modifying a NeRF representation based
on a single observation of a non-rigid transformed version of the original
scene. Our method defines the transformation as a 3D flow, specifically as a
weighted linear blending of rigid transformations of 3D anchor points that are
defined on the surface of the scene. In order to identify anchor points, we
introduce a novel correspondence algorithm that first matches RGB-based pairs,
then leverages multi-view information and 3D reprojection to robustly filter
false positives in two steps. We also introduce a new dataset for exploring the
problem of modifying a NeRF scene through a single observation. Our dataset (
https://github.com/nerfdeformer/nerfdeformer ) contains 113 synthetic scenes
leveraging 47 3D assets. We show that our proposed method outperforms NeRF
editing methods as well as diffusion-based methods, and we also explore
different methods for filtering correspondences.
