Research map / Image generation / Editing and style transfer
Learning Residual Images for Face Attribute Manipulation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply glasses removal to images in D1 | Proposed residual-image and dual-learning method · D1_m, eye landmarks | average normalized distance error: 0.03570 -> 0.03048 | Helped |
| Apply glasses removal to images in D1 | Proposed residual-image and dual-learning method · D1_m, rest landmarks | average normalized distance error: 0.04605 -> 0.04608 | No clear effect |
| Remove residual-image learning and generate the entire image (ablation) | Proposed method · CelebA glasses manipulation | image quality | Hurt |
| Remove dual learning by breaking the data-feed loop (ablation) | Proposed method · CelebA glasses manipulation | image quality | Hurt |
Closest papers by meaning
- Fader Networks:Manipulating Images by Sliding Attributes (NeurIPS 2017 · similarity 0.88)
- FaceController: Controllable Attribute Editing for Face in the Wild (AAAI 2021 · similarity 0.86)
- Mask-Guided Portrait Editing With Conditional GANs (CVPR 2019 · similarity 0.86)
- L2M-GAN: Learning To Manipulate Latent Space Semantics for Facial Attribute Editing (CVPR 2021 · similarity 0.86)
- Multi-Attribute Transfer via Disentangled Representation (AAAI 2019 · similarity 0.85)
- SDGAN: Disentangling Semantic Manipulation for Facial Attribute Editing (AAAI 2024 · similarity 0.84)
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