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

TL;DR
Blended-NeRF introduces a novel framework for local 3D scene editing and object blending in NeRFs using text prompts, volumetric blending, and geometric priors, enabling realistic, view-consistent modifications.
Contribution
It presents a new method for local 3D scene editing in NeRFs guided by text prompts and 3D ROI, with volumetric blending and geometric priors for improved realism.
Findings
Achieves realistic, multi-view consistent edits in 3D scenes.
Demonstrates flexibility in adding, removing, or altering objects.
Outperforms baselines in visual fidelity and diversity.
Abstract
Editing a local region or a specific object in a 3D scene represented by a NeRF or consistently blending a new realistic object into the scene is challenging, mainly due to the implicit nature of the scene representation. 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, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt, 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 blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent…
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Taxonomy
Topics3D Shape Modeling and Analysis · Computer Graphics and Visualization Techniques · Advanced Vision and Imaging
