Research map / Image generation / Editing and style transfer
BeautyGlow: On-Demand Makeup Transfer Framework With Reversible Generative Network
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use BeautyGlow for makeup transfer instead of Deep Image Analogy | Makeup transfer · Makeup transfer user study | preference: 60% vs 40% | Helped |
| Use BeautyGlow for makeup transfer instead of BeautyGAN | Makeup transfer · Makeup transfer user study | preference: 55% vs 45% | No clear effect |
| Use BeautyGlow for makeup transfer instead of PairedCycleGAN | Makeup transfer · Makeup transfer user study | preference: 45% vs 55% | Hurt |
| Add makeup loss (ablation) | BeautyGlow · Makeup transfer | makeup transfer quality | Helped |
| Add intra-domain loss (ablation) | BeautyGlow · Makeup transfer | makeup transfer quality | Helped |
Closest papers by meaning
- PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer (CVPR 2020 · similarity 0.88)
- Makeup Prior Models for 3D Facial Makeup Estimation and Applications (CVPR 2024 · similarity 0.87)
- Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth (CVPR 2024 · similarity 0.86)
- SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models (NeurIPS 2024 · similarity 0.86)
- DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection (CVPR 2024 · similarity 0.84)
- ACFun: Abstract-Concrete Fusion Facial Stylization (NeurIPS 2024 · similarity 0.83)
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