  We propose NeRF-Insert, a NeRF editing framework that allows users to make
high-quality local edits with a flexible level of control. Unlike previous work
that relied on image-to-image models, we cast scene editing as an in-painting
problem, which encourages the global structure of the scene to be preserved.
Moreover, while most existing methods use only textual prompts to condition
edits, our framework accepts a combination of inputs of different modalities as
reference. More precisely, a user may provide a combination of textual and
visual inputs including images, CAD models, and binary image masks for
specifying a 3D region. We use generic image generation models to in-paint the
scene from multiple viewpoints, and lift the local edits to a 3D-consistent
NeRF edit. Compared to previous methods, our results show better visual quality
and also maintain stronger consistency with the original NeRF.
