有关3D人脸重建的论文和代码，做个记录，以便与学习，和后来者阅读；黑桃符号(♠):表示 我已读过。
- FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction [] (百度)
- Towards High-Fidelity 3D Face Reconstruction From In-the-Wild Images Using Graph Convolutional Networks
- ReDA:Reinforced Differentiable Attribute for 3D Face Reconstruction
- AvatarMe: Realistically Renderable 3D Facial Reconstruction "In-the-Wild"
- Uncertainty-Aware Mesh Decoder for High Fidelity 3D Face Reconstruction
- Deep Facial Non-Rigid Multi-View Stereo
- Face Reconstruction from Voice using Generative dversarial Networks [paper]
- 3D Dense Face Alignment via Graph Convolution Networks [paper] （♠）
- 2DASL: Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning [paper]. [code] （♠）
- On learning 3d face morphable model from in-the-wild images [paper] (TPAMI)
- GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction [paper]. [code]
- Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision [paper]
- Dense 3D Face Decoding over 2500FPS: Joint Texture & Shape Convolutional Mesh Decoders [paper]
- MMFace: A Multi-Metric Regression Network for Unconstrained Face Reconstruction[paper].
- Towards High-Fidelity Nonlinear 3D Face Morphable Model [paper]. [code].[project]
- MVF-Net: Multi-View 3D Face Morphable Model Regression [paper]. [code] （♠）(多视图重建)
- RingNet: Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision [paper].[code]. [project]
- Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking [paper]. （♠）
- Speech2Face: Learning the Face Behind a Voice [paper]
- 3D Face Shape Regression From 2D Videos with Multi-reconstruction and Mesh Retrieval [paper] (3DFAW比赛论文)
- The 2 nd 3D Face Alignment in the Wild Challenge (3DFAW-Video): Dense Reconstruction From Video[paper] (3DFAW比赛论文， 从Video重建3D人脸 研究趋势)
- DF2Net: A Dense-Fine-Finer Network for Detailed 3D Face Reconstruction [paper]
- State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications [paper] (人脸重建综述)
- Deep Appearance Models for Face Rendering [paper]
- Multilinear Autoencoder for 3D Face Model Learning [paper]
- Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition [paper] (♠)
- Unsupervised Training for 3D Morphable Model Regression [paper]
- Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz [paper]
- Nonlinear 3D Face Morphable Model [paper]
- Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network [paper]. [code] (PRNet) （♠）
- Generating 3D faces using Convolutional Mesh Autoencoders [paper]
- Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition [paper]
- DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild [paper]
- Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network [paper]. [code]. [project] (3dmm_cnn) (♠)
- Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression [paper] [code] (VRN)
- Reconstruction-Based Disentanglement for Pose-invariant Face Recognition [paper]
- End-to-end 3D face reconstruction with deep neural networks [paper] (♠)
- Learning Detailed Face Reconstruction from a Single Image [paper]
- Photorealistic Facial Texture Inference Using Deep Neural Networks [paper] (纹理方面)
- Dense Face Alignment [paper]
- How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) [paper]
- Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation [paper]
- MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction [paper]
- 3D Face Reconstruction by Learning from Synthetic Data [paper]