Generative Face Completion
1. Motivation 以前的圖像補全算法都是從已知區域中複製 pattern，當補全對象是人臉時，圖像擁有相似 pattern的假設不成立。 2. Approach 2.1 Network architecture Network architecture Generator：生成器就是常見的 Encoder-decoder結構； Discriminator：判別器有兩個， Global d
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相關文章
- 1. GAN人臉修復--Generative Face Completion
- 2. Geometry-Aware Face Completion and Editing
- 3. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 4. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORK
- 5. Face Aging with Contextual Generative Adversarial Nets
- 6. Learning Oracle Attention for High-fidelity Face Completion
- 7. Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
- 8. Learning disentangling and fusing networks for face completion under structured occlusions
- 9. 2019 IJCNN之GAN（image transfer（face））：Attention-Guided Generative Adversarial Networks for Unsupervis
- 10. SC-FEGAN: Face Editing Generative Adversarial Network with User’s Sketch and Color