Research map / 3D vision / NeRF and radiance fields
Pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use pi-GAN instead of the strongest listed baseline, GRAF | 3D-aware image synthesis · CelebA 128×128 | FID: 41.1 -> 14.7 | Helped |
| use pi-GAN instead of the strongest listed baseline, GRAF | 3D-aware image synthesis · Cats 128×128 | FID: 28.9 -> 16.8 | Helped |
| use pi-GAN instead of the strongest listed baseline, GRAF | 3D-aware image synthesis · CARLA 128×128 | FID: 41.7 -> 29.2 | Helped |
| add sine activations instead of ReLU positional encoding, with concatenation conditioning (ablation) | pi-GAN radiance-field representation · CelebA 64×64 | FID: 32.0 -> 21.6 | Helped |
| add mapping-network FiLM conditioning instead of concatenation, with ReLU positional encoding (ablation) | pi-GAN radiance-field representation · CelebA 64×64 | FID: 32.0 -> 26.8 | Helped |
| add both sine activations and mapping-network FiLM conditioning (ablation) | pi-GAN radiance-field representation · CelebA 64×64 | FID: 32.0 -> 5.15 | Helped |
Closest papers by meaning
- Efficient Geometry-Aware 3D Generative Adversarial Networks (CVPR 2022 · similarity 0.92)
- 3D-Aware Image Synthesis via Learning Structural and Textural Representations (CVPR 2022 · similarity 0.91)
- Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase (NeurIPS 2023 · similarity 0.90)
- Multi-View Consistent Generative Adversarial Networks for 3D-Aware Image Synthesis (CVPR 2022 · similarity 0.90)
- Exemplar-Based Pattern Synthesis With Implicit Periodic Field Network (CVPR 2022 · similarity 0.90)
- What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs (CVPR 2024 · similarity 0.89)
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