End-to-end 3D face reconstruction with deep neural networks
Abstract 该文不采用利用rgb图和初始化3D面部表情渲染的方式, 是端对端的， 可以避免复杂的3D渲染.我们从两个方面综合DNN，利用多任务损失函数和融合DNN来改善面部表情重构；利用多任务损失函数， 可以将3D人脸重构分为3D人脸重构和3D人脸表情重构两个部分. 由于中性面部形状是类固定的，所以用网络的高层特征表示，而面部表情用低层或中层网络层特征表示。 Method 用一系列的
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相关文章
- 1. DeepID3: Face Recognition with Very Deep Neural Networks
- 2. 【DDFD】《Multi-view Face Detection Using Deep Convolutional Neural Networks》
- 3. Brain tumor segmentation with Deep Neural Networks
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- 5. AlexNet：ImageNet Classification with Deep Convolutional Neural Networks
- 6. ImageNet Classification with Deep Convolutional Neural Networks
- 7. Large Pose 3D Face Reconstruction 文章理解
- 8. 【Comprssion】《Deep Compression：Compressing Deep Neural Networks with Pruning，Trained QuantizationXXX》
- 9. 【论文整理】Face Related Papers and Code Collection Face Reconstruction Research
- 10. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks