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Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions
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We propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, our method uses an image-conditioned diffusion model (InstructPix2Pix) to iteratively edit the input images while optimizing the underlying scene, resulting in an optimized 3D scene that respects the edit instruction. We demonstrate that our proposed method is able to edit large-scale, real-world scenes, and is able to accomplish more realistic, targeted edits than prior work.
Forward citations
Cited by 3 Pith papers
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Perturb-and-Revise: Flexible 3D Editing with Generative Trajectories
Perturb-and-Revise edits 3D scenes by mixing a NeRF's trained parameters with random ones, running multi-view score distillation toward the edit prompt, and refining with identity-preserving gradients.
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NeRF Inpainting with Geometric Diffusion Prior and Balanced Score Distillation
GB-NeRF improves NeRF inpainting by fine-tuning a diffusion model on RGB-plus-normal image pairs and replacing standard score distillation with a two-term balanced loss.
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