• 人脸 Mean+PCA 实验
• MeshCNN: A Network with an Edge (TOG 2019)
• Nonlinear 3D Face Morphable Model (2018, 2019)
• Mesh Deform 相关论文的一些参考
• 使用3DMM进行人脸重建中的配准方法
• Windows 3dmm 实现
• (3DMMasSTN) 3D Morphable Models as Spatial Transformer Networks
• GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
• Related works 人脸生成
• 3D Guided Fine-Grained Face Manipulation
• 一些人脸重建的paper
• Automatic Unpaired Shape Deformation Transfer （SIGGRAPH ASIA 2018）
• Phase-Functioned Neural Networks for Character Control
• AIAnimation使用代码分析
• AI4Animation 工程
• AIAnimation 工程安装
• Machine Learning Techniques Lecture 15: Matrix Factorization
• Machine Learning Techniques Lecture 14: Radial Basis Function Network
• Machine Learning Techniques Lecture 13: Deep Learning
• Machine Learning Techniques Lecture 12: Neural Network
• Machine Learning Techniques Lecture 11: Gradient Boosted Decision Tree
• Machine Learning Techniques Lecture 10: Random Forest
• Machine Learning Techniques Lecture 9: Decision Tree
• Machine Learning Techniques Lecture 8: Adaptive Boosting
• Machine Learning Techniques Lecture 7: Blending and Bagging
• Machine Learning Techniques Lecture 6: Support Vector Regression
• Machine Learning Techniques Lecture 5: Kernel Logistic Regression
• Machine Learning Techniques Lecture 4: Soft-Margin Support Vector Machine
• Machine Learning Techniques Lecture 3: Kernel Support Vector Machine
• Machine Learning Techniques Lecture 2: Dual Support Vector Machine
• Machine Learning Techniques Lecture 1: Linear Support Vector Machine
• Machine Learning Foundations 16: Three Learning Principles
• Machine Learning Foundations 15: Validation
• Machine Learning Foundations 14: Regularization
• Machine Learning Foundations 13: Hazard of Overfitting
• Machine Learning Foundations 12: Nonlinear Transformation
• Machine Learning Foundations 11: Linear Models for Classification
• Machine Learning Foundations 10: Logistic Regression
• Machine Learning Foundations 9: Linear Regression
• Machine Learning Foundations 8: Noise and Error
• Machine Learning Foundations 7: The VC Dimension
• Machine Learning Foundations 6: Theory of Generalization
• Machine Learning Foundations 5: Training versus Testing
• Machine Learning Foundations 4: Feasibility of Learning
• Machine Learning Foundations 3: Types of Learning
• Machine Learning Foundations 2: Learning to Answer Yes/No
• Machine Learning Foundations 1: The Learning Problem
• Caffe 训练自有数据
• Caffe Notes
• Use Caffe windows(Microsoft)
• Book Note – Deep Learning