# Control-NeRF: Editable Feature Volumes for Scene Rendering and Manipulation

**Authors:** Verica Lazova, Vladimir Guzov, Kyle Olszewski, Sergey Tulyakov, Gerard Pons-Moll  
**arXiv:** [2204.10850](https://arxiv.org/abs/2204.10850)  
**Date:** 2022-04-22  
**Summary page:** https://www.summarizepaper.com/en/paper/2204.10850v1/

## Key points

- The paper presents a novel method for flexible 3D-aware image content manipulation while enabling high-quality novel view synthesis.
- NeRF-based approaches are effective for novel view synthesis but lack a 3D scene representation and are scene-specific which makes classical editing such as shape manipulation or combining scenes impossible.
- The authors propose coupling learnt scene-specific feature volumes with a scene agnostic neural rendering network to obtain interpretable and controllable scene representations.
- This hybrid representation decouples neural rendering from scene-specific geometry and appearance allowing them to generalize to new scenes by optimizing only the feature representation while keeping the parameters of the rendering network fixed.
- Control-NeRF enables users to edit complex 3D scenes with ease by manipulating and combining scenes through editing their corresponding feature volumes which can then be plugged into the rendering model to synthesize high-quality novel views.
- Various applications of Control-NeRF including mixing scenes, deforming objects, and inserting objects into scenes have been showcased by the authors while still producing photo-realistic results.
- Control-NeRF provides an innovative solution that bridges the gap between traditional 3D graphics editing tools and modern deep learning based methods for image synthesis with significant potential applications in fields such as virtual reality content creation and video game development.

## Summary

In their paper titled "Control-NeRF: Editable Feature Volumes for Scene Rendering and Manipulation," authors Verica Lazova, Vladimir Guzov, Kyle Olszewski, Sergey Tulyakov, and Gerard Pons-Moll present a novel method for flexible 3D-aware image content manipulation while enabling high-quality novel view synthesis. NeRF-based approaches are effective for novel view synthesis as they memorize the radiance for every point in a scene within a neural network. However, these models lack a 3D scene representation and are scene-specific which makes classical editing such as shape manipulation or combining scenes impossible. To address this limitation, the authors propose coupling learnt scene-specific feature volumes with a scene agnostic neural rendering network to obtain interpretable and controllable scene representations. This hybrid representation decouples neural rendering from scene-specific geometry and appearance allowing them to generalize to new scenes by optimizing only the feature representation while keeping the parameters of the rendering network fixed. The authors demonstrate that their approach is more flexible as it can be easily applied to new scenes without requiring extensive knowledge of computer graphics or machine learning techniques. Moreover, Control-NeRF enables users to edit complex 3D scenes with ease by manipulating and combining scenes through editing their corresponding feature volumes which can then be plugged into the rendering model to synthesize high-quality novel views. Various applications of Control-NeRF including mixing scenes, deforming objects and inserting objects into scenes have been showcased by the authors while still producing photo-realistic results. Overall, Control-NeRF provides an innovative solution that bridges the gap between traditional 3D graphics editing tools and modern deep learning based methods for image synthesis with significant potential applications in fields such as virtual reality content creation and video game development.