  Text-driven localized editing of 3D objects is particularly difficult as
locally mixing the original 3D object with the intended new object and style
effects without distorting the object's form is not a straightforward process.
To address this issue, we propose a novel NeRF-based model, Blending-NeRF,
which consists of two NeRF networks: pretrained NeRF and editable NeRF.
Additionally, we introduce new blending operations that allow Blending-NeRF to
properly edit target regions which are localized by text. By using a pretrained
vision-language aligned model, CLIP, we guide Blending-NeRF to add new objects
with varying colors and densities, modify textures, and remove parts of the
original object. Our extensive experiments demonstrate that Blending-NeRF
produces naturally and locally edited 3D objects from various text prompts. Our
project page is available at https://seokhunchoi.github.io/Blending-NeRF/

Computer Vision and Pattern Recognition;Artificial Intelligence;Graphics