FENeRF: Face Editing in Neural Radiance Fields
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Abstract (arXiv)
Previous portrait image generation methods roughly fall into two categories: 2D GANs and 3D-aware GANs. 2D GANs can generate high fidelity portraits but with low view consistency. 3D-aware GAN methods can maintain view consistency but their generated images are not locally editable. To overcome these limitations, we propose FENeRF, a 3D-aware generator that can produce view-consistent and locally-editable portrait images. Our method uses two decoupled latent codes to generate corresponding facial semantics and texture in a spatial aligned 3D volume with shared geometry. Benefiting from such underlying 3D representation, FENeRF can jointly render the boundary-aligned image and semantic mask and use the semantic mask to edit the 3D volume via GAN inversion. We further show such 3D representation can be learned from widely available monocular image and semantic mask pairs. Moreover, we reveal that joint learning semantics and texture helps to generate finer geometry. Our experiments demonstrate that FENeRF outperforms state-of-the-art methods in various face editing tasks.
Key facts. Cited by 112 papers in the map (data through 7 Oct 2026). Builds on 47 of its 55 references that are in the map. First posted on arXiv 30 Nov 2021.
Builds on
47 of 55 references.
References · 47
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- 2021High-Fidelity GAN Inversion for Image Attribute Editingcited by 197
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Citing papers per year: 2021: 2; 2022: 33; 2023: 40; 2024: 23; 2025: 9; 2026: 5.
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- 2025Using Gaussian Splats to Create High-Fidelity Facial Geometry and Texture18 Dec 2025
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