3D【11】人臉重建：Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition
這篇文章的思想跟Multilinear Autoencoder for 3D Face Model Learning 本質上是完全一樣的。將人臉重建看成是一個線性問題，然後求解線性係數和特徵向量。而且訓練方法也是一樣的。分三步：1、預訓練encoder；2、預訓練decoder；3、訓練整個模型。 兩篇文章的區別是：該論文用的是3DMM的理論，而上面我提到的那篇用的是高階SVD張量分解理論。 我們
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相關文章
- 1. 人臉對齊（十八）--Joint Face Alignment and 3D Face Reconstruction
- 2. 3D人臉重建：《Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network》
- 3. 3D【9】人臉重建:Joint3D Face Reconstruction and Dense Alignment with Position Map Regression
- 4. 3D【6】人臉重建:Multilinear Autoencoder for 3D Face Model Learning
- 5. 人臉識別-3D:Learning from Millions of 3D Scans for Large-scale 3D Face Recognition
- 6. Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
- 7. 3D【3】人臉重建:Image Based 3D Face Reconstruction: A Survey閱讀筆記
- 8. 3D【4】人臉重建:Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric
- 9. 人臉識別論文筆記 Reconstruction-Based Disentanglement for Pose-invariant Face Recognition
- 10. 3D Face Reconstruction from a Single Image