title: Blended-NeRF: Zero-Shot Object Generation and Blending in Existing
    Neural Radiance Fields
summary: 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.
paper_authors._id: 6494fb709ed3dd7d168e7dad
paper_authors.name: Ori Gordon
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-15T08:19:10.338Z
paper_authors._id: 62f65a82ffd6a0853ee2c9b1
paper_authors.avatarUrl: /avatars/cebd89ab47a83ed3b5e9423dd7d856b7.svg
paper_authors.fullname: Gordon
paper_authors.type: user
paper_authors.user: orig333
paper_authors._id: 6494fb709ed3dd7d168e7dae
paper_authors.name: Omri Avrahami
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-26T07:33:09.781Z
paper_authors._id: 62f6ab4fc3372328414c8689
paper_authors.avatarUrl: /avatars/4f728a5b70c9fe4a64e80e2b643ca620.svg
paper_authors.fullname: Omri Avrahami
paper_authors.type: user
paper_authors.user: omriav
paper_authors._id: 6494fb709ed3dd7d168e7daf
paper_authors.name: Dani Lischinski
paper_title: Blended-NeRF: Zero-Shot Object Generation and Blending in Existing
    Neural Radiance Fields
paper_summary: 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.
paper_ai_summary: Blended-NeRF framework enables robust 3D scene editing using text prompts or image patches with a volumetric blending technique, supporting various editing tasks.
paper_ai_keywords: NeRF text prompts image patches 3D ROI box pretrained language-image model 3D MLP model volumetric blending technique geometric priors 3D augmentations multi-view consistent results 3D editing applications adding objects removing/replacing/altering objects texture conversion