Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction

时间 2020-12-23

标签 cvpr2019 神经网络栏目系统网络繁體版

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Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction 图1：提出的深度拟合方法可以从单个图像重建高质量的纹理和几何，并具有精确的身份恢复。图中和本文其余部分的重构由大小为700个浮点的向量表示，并且进行了渲染，没有任何特殊效果。我们想强调的是，所描绘的纹理是由我们的模型重建的，没有直接从图像中

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1. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORK
2. Learning Oracle Attention for High-fidelity Face Completion
3. FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
4. SaGAN:Generative Adversarial Network with Spatial Attention for Face Attribute Editing
5. Generative Adversarial Network (GAN) papers (2018.2.4 更新）
6. Face Aging with Contextual Generative Adversarial Nets
7. Finding Tiny Faces in the Wild with Generative Adversarial Network
8. ReDA: Reinforced Differentiable Attribute for 3D Face Reconstruction
9. Generative Adversarial Network 3 WGAN/ EBGAN
10. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
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1. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORK
2. Learning Oracle Attention for High-fidelity Face Completion
3. FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
4. SaGAN:Generative Adversarial Network with Spatial Attention for Face Attribute Editing
5. Generative Adversarial Network (GAN) papers (2018.2.4 更新）
6. Face Aging with Contextual Generative Adversarial Nets
7. Finding Tiny Faces in the Wild with Generative Adversarial Network
8. ReDA: Reinforced Differentiable Attribute for 3D Face Reconstruction
9. Generative Adversarial Network 3 WGAN/ EBGAN
10. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS

>>更多相关文章<<