Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions
Berkeley College · University of California, Berkeley
Abstract
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. Result videos can be found on the project website: https://instruct-nerf2nerf.github.io.
Citation impact
- FWCI
- 27.83
- Percentile
- 100%
- References
- 51
Authors
5- AHAyaan HaqueCorresponding
Berkeley College, University of California, Berkeley
- MTMatthew Tancik
University of California, Berkeley, Berkeley College
- AAAlexei A. Efros
Berkeley College, University of California, Berkeley
- AHAleksander Holynski
Berkeley College, University of California, Berkeley
- AKAngjoo Kanazawa
Berkeley College, University of California, Berkeley
Topics & keywords
- Computer science
- Computer vision
- Computer graphics (images)
- Artificial intelligence
- Image editing
- Image (mathematics)
- Scale (ratio)