Generative NeRF-to-NeRF Translation: A Unified Framework for Versatile 3D Scene Editing

Core Concepts

GenN2N, a unified framework for NeRF-to-NeRF translation, enables a range of 3D NeRF editing tasks, including text-driven editing, colorization, super-resolution, inpainting, etc. by leveraging 2D image-to-image translation methods and modeling the distribution of 3D edited NeRFs.

Abstract

The paper introduces GenN2N, a unified framework for NeRF-to-NeRF translation that can handle various NeRF editing tasks. Unlike previous task-specific approaches, GenN2N uses a plug-and-play 2D image-to-image translator for 2D editing and integrates the results into 3D NeRF space.
To address the challenge of ensuring 3D consistency, the authors propose modeling the distribution of 3D edited NeRFs from 2D edited images. Specifically, they design a 3D VAE-GAN that incorporates a differentiable volume renderer to connect 2D content creation with 3D generation. Additionally, a contrastive learning scheme is introduced to disentangle the 3D edits and 2D camera views.
After optimization, users can sample from the conditional generative model to obtain diverse 3D editing results with high rendering quality and multi-view consistency. Experiments demonstrate that GenN2N outperforms existing task-specific methods on various editing tasks, including text-driven editing, colorization, super-resolution, and inpainting, in terms of efficiency, quality, and diversity.

Stats

"Given N multi-view images {Ii}N−1
i=0 of a scene, we first use Nerfstudio [35] to train the original NeRF."
"For each view i ∈[0, N −1], we generate M edited images, resulting in a group of translated image set {{Sj
i}M−1
j=0 }N−1
i=0 ."

Quotes

"GenN2N, a unified NeRF-to-NeRF translation framework for various NeRF editing tasks such as text-driven editing, colorization, super-resolution, inpainting, etc."
"Our key idea is to embrace the stochastic nature of content editing by modeling the distribution of the edits in the 3D NeRF space."
"We design a 3D VAE-GAN that incorporates a differentiable volume renderer to connect 2D content creation with 3D generation."

Key Insights Distilled From

GenN2N

by Xiangyue Liu... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02788.pdf

Deeper Inquiries

How can GenN2N be extended to handle more complex 3D editing tasks, such as geometry deformation or object manipulation?

GenN2N can be extended to handle more complex 3D editing tasks by incorporating advanced techniques for geometry deformation and object manipulation. One approach could be to integrate deformable neural networks or mesh-based representations into the framework. By incorporating deformable neural networks, GenN2N can learn to deform the underlying 3D geometry based on the editing instructions or input images. This would enable tasks such as shape manipulation, object deformation, and even animation within the 3D scene. Additionally, integrating mesh-based representations would allow for more detailed and fine-grained control over the geometry of the 3D objects, enabling tasks like detailed object manipulation, sculpting, and intricate geometry editing.

What are the potential limitations of the current contrastive learning scheme in disentangling 3D edits and 2D camera views, and how could it be further improved?

The current contrastive learning scheme in GenN2N may have limitations in fully disentangling 3D edits and 2D camera views, leading to potential issues such as residual viewpoint dependencies in the latent space. One limitation could be the sensitivity of the contrastive loss to noise or variations in the input images, which may affect the disentanglement process. Additionally, the contrastive loss may struggle with capturing subtle variations in 3D edits that are not explicitly represented in the 2D images.
To improve the contrastive learning scheme, several strategies can be implemented. One approach is to incorporate additional regularization techniques that encourage the latent space to be more robust to variations in input images. This could involve introducing stronger regularization terms that penalize deviations in the latent space caused by viewpoint changes. Furthermore, leveraging more advanced contrastive learning methods, such as InfoNCE loss or MoCo, could enhance the disentanglement of 3D edits and camera views by providing more robust representations of the editing instructions.

Given the versatility of GenN2N, how could it be applied to other 3D content creation and editing domains beyond NeRF, such as voxel-based or mesh-based representations?

The versatility of GenN2N opens up possibilities for its application in various 3D content creation and editing domains beyond NeRF, including voxel-based or mesh-based representations. To apply GenN2N to these domains, adaptations and enhancements can be made to accommodate the specific characteristics of voxel-based or mesh-based representations.
For voxel-based representations, GenN2N could be modified to generate and edit volumetric data directly. By incorporating voxel-based rendering techniques and volumetric editing tools, GenN2N could enable tasks such as volumetric shape editing, texture painting in 3D space, and voxel-level manipulation.
In the case of mesh-based representations, GenN2N could be extended to handle mesh deformation, texture mapping, and surface editing. By integrating mesh processing algorithms and mesh editing tools, GenN2N could facilitate tasks like mesh sculpting, surface reconstruction, and detailed object manipulation in the mesh domain.
Overall, by adapting GenN2N to voxel-based or mesh-based representations, it can offer a comprehensive and flexible framework for a wide range of 3D content creation and editing tasks across different representation formats.