Blended-NeRF: Zero-Shot Object Generation and Blending in Existing
  Neural Radiance Fields
Authors: name: Ori Gordon, name: Omri Avrahami, name: Dani Lischinski

Editing a local region or a specific object in a 3D scene represented by a
NeRF is challenging, mainly due to the implicit nature of the scene
representation. Consistently blending a new realistic object into the scene
adds an additional level of difficulty. We present Blended-NeRF, a robust and
flexible framework for editing a specific region of interest in an existing
NeRF scene, based on text prompts or image patches, along with a 3D ROI box.
Our method leverages a pretrained language-image model to steer the synthesis
towards a user-provided text prompt or image patch, along with a 3D MLP model
initialized on an existing NeRF scene to generate the object and blend it into
a specified region in the original scene. We allow local editing by localizing
a 3D ROI box in the input scene, and seamlessly blend the content synthesized
inside the ROI with the existing scene using a novel volumetric blending
technique. To obtain natural looking and view-consistent results, we leverage
existing and new geometric priors and 3D augmentations for improving the visual
fidelity of the final result.
  We test our framework both qualitatively and quantitatively on a variety of
real 3D scenes and text prompts, demonstrating realistic multi-view consistent
results with much flexibility and diversity compared to the baselines. Finally,
we show the applicability of our framework for several 3D editing applications,
including adding new objects to a scene, removing/replacing/altering existing
objects, and texture conversion.