# NeRFFaceEditing: Disentangled Face Editing in Neural Radiance Fields

arXiv: https://arxiv.org/abs/2211.07968 · HTML: https://mlsanity.com/paper/2211.07968/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 54 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 30 of 44)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields](https://mlsanity.com/paper/2011.12100/) (2020)
- [pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2012.00926/) (2020)
- [GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2007.02442/) (2020)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis](https://mlsanity.com/paper/2110.08985/) (2021)
- [Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction](https://mlsanity.com/paper/2012.03065/) (2020)
- [Pivotal Tuning for Latent-based Editing of Real Images](https://mlsanity.com/paper/2106.05744/) (2021)
- [StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows](https://mlsanity.com/paper/2008.02401/) (2020)
- [AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis](https://mlsanity.com/paper/2103.11078/) (2021)
- [StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation](https://mlsanity.com/paper/2112.11427/) (2021)
- [On the Detection of Digital Face Manipulation](https://mlsanity.com/paper/1910.01717/) (2019)
- [I M Avatar: Implicit Morphable Head Avatars from Videos](https://mlsanity.com/paper/2112.07471/) (2021)
- [HeadNeRF: A Real-time NeRF-based Parametric Head Model](https://mlsanity.com/paper/2112.05637/) (2021)
- [Editing Conditional Radiance Fields](https://mlsanity.com/paper/2105.06466/) (2021)
- [GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation](https://mlsanity.com/paper/2112.08867/) (2021)
- [CodeNeRF: Disentangled Neural Radiance Fields for Object Categories](https://mlsanity.com/paper/2109.01750/) (2021)
- [Nonlinear 3D Face Morphable Model](https://mlsanity.com/paper/1804.03786/) (2018)
- [EditGAN: High-Precision Semantic Image Editing](https://mlsanity.com/paper/2111.03186/) (2021)
- [Demystifying Neural Style Transfer](https://mlsanity.com/paper/1701.01036/) (2017)

## Built on by (most cited first, 30 of 33)

- [One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field](https://mlsanity.com/paper/2304.05097/) (2023)
- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF](https://mlsanity.com/paper/2311.13099/) (2023)
- [Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering](https://mlsanity.com/paper/2401.15318/) (2024)
- [SphereHead: Stable 3D Full-head Synthesis with Spherical Tri-plane Representation](https://mlsanity.com/paper/2404.05680/) (2024)
- [Control3Diff: Learning Controllable 3D Diffusion Models from Single-view Images](https://mlsanity.com/paper/2304.06700/) (2023)
- [3D-aware Conditional Image Synthesis](https://mlsanity.com/paper/2302.08509/) (2023)
- [Rip-NeRF: Anti-aliasing Radiance Fields with Ripmap-Encoded Platonic Solids](https://mlsanity.com/paper/2405.02386/) (2024)
- [LPFF: A Portrait Dataset for Face Generators Across Large Poses](https://mlsanity.com/paper/2303.14407/) (2023)
- [AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections](https://mlsanity.com/paper/2309.02186/) (2023)
- [3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses](https://mlsanity.com/paper/2307.14770/) (2023)
- [HyperStyle3D: Text-Guided 3D Portrait Stylization via Hypernetworks](https://mlsanity.com/paper/2304.09463/) (2023)
- [LC-NeRF: Local Controllable Face Generation in Neural Randiance Field](https://mlsanity.com/paper/2302.09486/) (2023)
- [LatentSwap3D: Semantic Edits on 3D Image GANs](https://mlsanity.com/paper/2212.01381/) (2022)
- [Generating Editable Head Avatars with 3D Gaussian GANs](https://mlsanity.com/paper/2412.19149/) (2024)
- [MaTe3D: Mask-guided Text-based 3D-aware Portrait Editing](https://mlsanity.com/paper/2312.06947/) (2023)
- [SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation](https://mlsanity.com/paper/2403.10166/) (2024)
- [PVP: Personalized Video Prior for Editable Dynamic Portraits using StyleGAN](https://mlsanity.com/paper/2306.17123/) (2023)
- [A General Implicit Framework for Fast NeRF Composition and Rendering](https://mlsanity.com/paper/2308.04669/) (2023)
- [3D-SSGAN: Lifting 2D Semantics for 3D-Aware Compositional Portrait Synthesis](https://mlsanity.com/paper/2401.03764/) (2024)
- [Revealing Directions for Text-guided 3D Face Editing](https://mlsanity.com/paper/2410.04965/) (2024)
- [SuperNeRF-GAN: A Universal 3D-Consistent Super-Resolution Framework for Efficient and Enhanced 3D-Aware Image Synthesis](https://mlsanity.com/paper/2501.06770/) (2025)
- [Challenges and Opportunities in 3D Content Generation](https://mlsanity.com/paper/2405.15335/) (2024)
- [HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation](https://mlsanity.com/paper/2411.12832/) (2024)
- [StyleMorpheus: A Style-Based 3D-Aware Morphable Face Model](https://mlsanity.com/paper/2503.11792/) (2025)
- [FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields](https://mlsanity.com/paper/2503.17095/) (2025)
- [SemFaceEdit: Semantic Face Editing on Generative Radiance Manifolds](https://mlsanity.com/paper/2506.22833/) (2025)
- [Pomo3D: 3D-Aware Portrait Accessorizing and More](https://mlsanity.com/paper/2409.14430/) (2024)
- [GCRayDiffusion: Pose-Free Surface Reconstruction via Geometric Consistent Ray Diffusion](https://mlsanity.com/paper/2503.22349/) (2025)
- [Using Gaussian Splats to Create High-Fidelity Facial Geometry and Texture](https://mlsanity.com/paper/2512.16397/) (2025)

## Abstract (arXiv)

Recent methods for synthesizing 3D-aware face images have achieved rapid development thanks to neural radiance fields, allowing for high quality and fast inference speed. However, existing solutions for editing facial geometry and appearance independently usually require retraining and are not optimized for the recent work of generation, thus tending to lag behind the generation process. To address these issues, we introduce NeRFFaceEditing, which enables editing and decoupling geometry and appearance in the pretrained tri-plane-based neural radiance field while retaining its high quality and fast inference speed. Our key idea for disentanglement is to use the statistics of the tri-plane to represent the high-level appearance of its corresponding facial volume. Moreover, we leverage a generated 3D-continuous semantic mask as an intermediary for geometry editing. We devise a geometry decoder (whose output is unchanged when the appearance changes) and an appearance decoder. The geometry decoder aligns the original facial volume with the semantic mask volume. We also enhance the disentanglement by explicitly regularizing rendered images with the same appearance but different geometry to be similar in terms of color distribution for each facial component separately. Our method allows users to edit via semantic masks with decoupled control of geometry and appearance. Both qualitative and quantitative evaluations show the superior geometry and appearance control abilities of our method compared to existing and alternative solutions.