Editing 3D Scenes with Instructions
Instruct-NeRF2NeRF enables instruction-based editing of NeRFs via a 2D diffusion model
First install nerfstudio dependencies. Then run:
pip install git+https://github.com/ayaanzhaque/instruct-nerf2nerf
Details for running Instruct-NeRF2NeRF (built with Nerfstudio!) can be found here. Once installed, run:
ns-train in2n --help
Three variants of Instruct-NeRF2NeRF are provided:
Full model, used in paper
Half precision model
Half prevision with no LPIPS
Instruct-NeRF2NeRF is a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, the 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. The paper demonstrates that their method is able to edit large-scale, real-world scenes, and is able to accomplish more realistic, targeted edits than prior work.
This section will walk through each component of the Instruct-NeRF2NeRF method.
How it Works#
Instruct-NeRF2NeRF gradually updates a reconstructed NeRF scene by iteratively updating the dataset images while training the NeRF:
An image is rendered from the scene at a training viewpoint.
It is edited by InstructPix2Pix given a global text instruction.
The training dataset image is replaced with the edited image.
The NeRF continues training as usual.
Editing Images with InstructPix2Pix#
InstructPix2Pix is an image-editing diffusion model which can be prompted using text instructions. More details on InstructPix2Pix can be found here.
Traditionally, at inference time, InstructPix2Pix takes as input random noise and is conditioned on an image (the image to edit) and a text instruction. The strength of the edit can be controlled using the image and text classifier-free guidance scales.
To update a dataset image a given viewpoint, Instruct-NeRF2NeRF first takes the original, unedited training image as image conditioning and uses the global text instruction as text conditioning. The main input to the diffusion model is a noised version of the current render from the given viewpoint. The noise is sampled from a normal distribution and scaled based on a randomly chosen timestep. Then InstructPix2Pix slowly denoises the rendered image by predicting the noised version of the image at previous timesteps until the image is fully denoised. This will produce an edited version of the input image.
This process mixes the information of the diffusion model, which attempts to edit the image, the current 3D structure of the NeRF, and view-consistent information from the unedited, ground-truth images. By combining this set of information, the edit is respected while maintaining 3D consistency.
The code snippet for how an image is edited in the pipeline can be found here.
Iterative Dataset Update#
When NeRF training starts, the dataset consists of the original, unedited images used to train the original scene. These images are saved separately to use as conditioning for InstructPix2Pix. At each optimization step, some number of NeRF optimization steps are performed, and then some number of images (often just one) are updated. The images are randomly ordered prior to training and then at each step, the images are chosen in order to edit. Once an image has been edited, it is replaced in the dataset. Importantly, at each NeRF step, rays are sampled across the entire dataset, meaning there is a mixed source of supervision between edited images and unedited images. This allows for a gradual optimization that balances maintaining the 3D structure and consistency of the NeRF as well as performing the edit.
At early iterations of this process, the edited images may be inconsistent with one another, as InstructPix2Pix often doesn’t perform consistent edits across viewpoints. However, over time, since images are edited using the current render of the NeRF, the edits begin to converge towards a globally consistent depiction of the underlying scene. Here is an example of how the underlying dataset evolves and becomes more consistent.
The traditional method for supervising NeRFs using diffusion models is to use a Score Distillation Sampling (SDS) loss, as proposed in DreamFusion. The Iterative Dataset Update method can be viewed as a variant of SDS, as instead of updating a discrete set of images at each step, the loss is a mix of rays from various viewpoints which are edited to varying degrees. The results show that this leads to higher quality performance and more stable optimization.