End-to-end 3D face reconstruction with deep neural networks
Abstract 該文不採用利用rgb圖和初始化3D面部表情渲染的方式, 是端對端的， 可以避免複雜的3D渲染.我們從兩個方面綜合DNN，利用多任務損失函數和融合DNN來改善面部表情重構；利用多任務損失函數， 可以將3D人臉重構分爲3D人臉重構和3D人臉表情重構兩個部分. 由於中性面部形狀是類固定的，所以用網絡的高層特徵表示，而面部表情用低層或中層網絡層特徵表示。 Method 用一系列的
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相關文章
- 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
- 4. ImageNet Classification with Deep Convolutional Neural Networks
- 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. 【論文整理】Face Related Papers and Code Collection Face Reconstruction Research
- 10. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks