Research map / 3D vision / NeRF and radiance fields
Semantic 3D-Aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use compositional semantic-region neural radiance fields with residual SDF representation | CNeRF · FFHQ | FID | Mixed |
| Replace residual SDF representation with original volume density (ablation) | CNeRF · FFHQ | 3D reconstruction consistency | Hurt |
| Replace residual SDF representation with direct SDF proxy (ablation) | CNeRF · FFHQ | 3D reconstruction consistency | Hurt |
| Add semantic discriminative loss and the Semantic-Discriminator (ablation) | CNeRF · FFHQ | semantic part control | Helped |
| Add shape-texture decoupling loss (ablation) | CNeRF · FFHQ | shape-texture disentanglement | Helped |
| Use independent semantic-region latent codes for local manipulation | CNeRF · FFHQ semantic editing | semantic part controllability | Helped |
Closest papers by meaning
- FENeRF: Face Editing in Neural Radiance Fields (CVPR 2022 · similarity 0.91)
- InstructPix2NeRF: Instructed 3D Portrait Editing from a Single Image (ICLR 2024 · similarity 0.89)
- GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D Scene Understanding (CVPR 2024 · similarity 0.89)
- In-N-Out: Faithful 3D GAN Inversion with Volumetric Decomposition for Face Editing (CVPR 2024 · similarity 0.88)
- FPRF: Feed-Forward Photorealistic Style Transfer of Large-Scale 3D Neural Radiance Fields (AAAI 2024 · similarity 0.87)
- SIGNeRF: Scene Integrated Generation for Neural Radiance Fields (CVPR 2024 · similarity 0.87)
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