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Facial Inpainting
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Facial inpainting (or face completion) is the task of generating plausible facial structures for missing pixels in a face image.
<span style="color:grey; opacity: 0.6">( Image credit: SymmFCNet )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Does Generative Face Completion Help Face Recognition?
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
Cross-spectral Face Completion for NIR-VIS Heterogeneous Face Recognition
Detecting Overfitting of Deep Generative Networks via Latent Recovery
Learning Symmetry Consistent Deep CNNs for Face Completion
Face Completion with Semantic Knowledge and Collaborative Adversarial Learning
On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs
Improved Techniques for GAN based Facial Inpainting
Superimposition-guided Facial Reconstruction from Skull
Geometry-Aware Face Completion and Editing
Identity Preserving Face Completion for Large Ocular Region Occlusion
Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart
High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks
Side Information for Face Completion: a Robust PCA Approach
Learning Disentangling and Fusing Networks for Face Completion Under Structured Occlusions
r-BTN: Cross-domain Face Composite and Synthesis from Limited Facial Patches
Generative Face Completion
DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification
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