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Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions

About

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.

Ayaan Haque, Matthew Tancik, Alexei A. Efros, Aleksander Holynski, Angjoo Kanazawa• 2023

Related benchmarks

TaskDatasetResultRank
NeRF ColorizationLLFF
CF45.599
8
Portrait EditingTensor4D static scenes
CLIP Similarity0.2989
7
Super-ResolutionLLFF
PSNR20.299
6
Local 3D EditingEvaluation dataset unseen 3D assets (test)
CLIP Similarity0.253
6
Global 3D EditingEvaluation dataset unseen 3D assets (test)
CLIP Similarity0.239
6
Text-driven NeRF EditingFace, Fangzhou, and Farm (test)
CLIP Dir Sim0.2021
5
Novel-view stylization53 stylizations (Instruct-NeRF2NeRF, GaussCtrl, ScanNet++, Mip-NeRF360, and new scenes) (full evaluation set)
CLIP Direction Similarity0.098
5
Object Insertion35 unique edits (5 scenes x 7 objects) (test)
CLIPScore0.2347
5
Stylization Semantic AlignmentRodin 35 examples
CLIP-IQA23.93
5
3D Object EditingSynthetic 3D Fashion Objects (test)
CLIP-Dir-SimViT (B/32)0.0583
4
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