Research map / Image generation / Editing and style transfer
Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed latent-direction method instead of Voynov et al. | StyleGAN2 face editing · CelebA-HQ faces, Smile (|ε̂| in (0, .3]) | change in other attributes: .115 -> .085 | Helped |
| Use the proposed latent-direction method instead of Voynov et al. | StyleGAN2 face editing · CelebA-HQ faces, Smile (|ε̂| in (0, .3]) | image identity similarity: .979 -> .995 | Helped |
| Train multiple attribute directions jointly rather than one direction at a time (ablation) | Proposed method · Face and scene image editing | unintended visual changes | Helped |
| Use a local transformation rather than a global direction (ablation) | Proposed method on PGGAN · Face attribute editing | visual editing quality | Helped |
| Use an MLP direction function rather than a linear-layer direction function (ablation) | Local transformation on PGGAN · Face attribute editing | visual editing quality | No clear effect |
| Train with the full loss rather than the regression loss alone (ablation) | MLP local transformation on PGGAN · Face attribute editing | training stability | Helped |
Closest papers by meaning
- Navigating the GAN Parameter Space for Semantic Image Editing (CVPR 2021 · similarity 0.91)
- Exploiting Spatial Dimensions of Latent in GAN for Real-Time Image Editing (CVPR 2021 · similarity 0.90)
- Using latent space regression to analyze and leverage compositionality in GANs (ICLR 2021 · similarity 0.90)
- Interpreting the Latent Space of GANs for Semantic Face Editing (CVPR 2020 · similarity 0.90)
- EditGAN: High-Precision Semantic Image Editing (NeurIPS 2021 · similarity 0.90)
- Reference-Based 3D-Aware Image Editing with Triplanes (CVPR 2025 · similarity 0.88)
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