update readme

Author: yfeng95

3D Face Papers.md

@@ -438,155 +438,155 @@
 
 > more in computer graphics  
 
-1. **High-Quality Single-Shot Capture of Facial Geometry**(TOG2010, ETHZ, Disney)
+- High-Quality Single-Shot Capture of Facial Geometry(TOG2010, ETHZ, Disney)
 
    `cg, high-detail,stereo system, calibration, surface refinement, normal direction, mesoscopic  `
 
-2. Multiview Face Capture using Polarized Spherical Gradient Illumination(TOG2011)
+- Multiview Face Capture using Polarized Spherical Gradient Illumination(TOG2011)
 
    `image collecitons`
 
-3. High-Quality Passive Facial Performance Capture using Anchor Frames(SIGGRAPH2011, ETHZ, Disney)
+- High-Quality Passive Facial Performance Capture using Anchor Frames(SIGGRAPH2011, ETHZ, Disney)
 
    `cg, stereo,anchor frame, tracking, mesh progration, physical movement, motion estimation, refinement `
 
-4. Lightweight binocular facial perfor- mance capture under uncontrolled lighting(TOG2012, MPI)
+- Lightweight binocular facial perfor- mance capture under uncontrolled lighting(TOG2012, MPI)
 
    `cg, high-detail, stereo, template,flow,data term, geometry term, smoothness term, mesh tracking, motion refinement, shape refinement, sfs  `
 
-5. Reconstructing Detailed Dynamic Face Geometry from Monocular Video(TOG2013,  MPI)
+- Reconstructing Detailed Dynamic Face Geometry from Monocular Video(TOG2013,  MPI)
 
    `cg, dynamic, high-detail, blend model, sparse correspondence, dense correspondence(appearance matching, LBP), pose estimation , shape refinement, sfs   `
 
-6. 3D Shape Regression for Real-time Facial Animation(TOG2013, ZJU)
+- 3D Shape Regression for Real-time Facial Animation(TOG2013, ZJU)
 
-7. Real-Time High-Fidelity Facial Performance Capture (TOG2015, ZJU)
+- Real-Time High-Fidelity Facial Performance Capture (TOG2015, ZJU)
 
    `cg, landmarks, optical flow, train a regressor to learn detail  `
 
-8. Dynamic 3D Avatar Creation from Hand-held Video Input(TOG2015,  EPEL)
+- Dynamic 3D Avatar Creation from Hand-held Video Input(TOG2015,  EPEL)
 
    `cg, dynamic, mobile, high-detail, avatar, 3dmm,sparse correspondence, eye mesh, tracking, refinement, sfs, detail map     ` 
 
-9. Reconstruction of Personalized 3D Face Rigs from Monocular Video(TOG2016,  MPI)
+- Reconstruction of Personalized 3D Face Rigs from Monocular Video(TOG2016,  MPI)
 
    `parametric shape prior, coarse-scale reconstruction, fine-scale(sfs), coase->medium->fine, 3dmm, corrective   `
 
-10. Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks（ASCA2017,  USC)
+- Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks（ASCA2017,  USC)
 
-11. Multi-View Stereo on Consistent Face Topology(EG2017, USC)
+- Multi-View Stereo on Consistent Face Topology(EG2017, USC)
 
-    `cg, high-detail, landmarks, template, pose estimation, refinement`
+   `cg, high-detail, landmarks, template, pose estimation, refinement`
 
-12. Avatar Digitization From a Single Image For Real-Time Rendering(SIGGRAPH Asia 2017, USC)
+- Avatar Digitization From a Single Image For Real-Time Rendering(SIGGRAPH Asia 2017, USC)
 
-    `cg, avatar, segmentation, head, hair, 3DMM, landmarks, texture completion   `
+   `cg, avatar, segmentation, head, hair, 3DMM, landmarks, texture completion   `
 
-13. Learning a model of facial shape and expression from 4D scans(TOG2017, USC, MPI)
+- Learning a model of facial shape and expression from 4D scans(TOG2017, USC, MPI)
 
-14. DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling(SIGGRAPH2017)  
+- DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling(SIGGRAPH2017)  
 
-15. High-Fidelity Facial Reflectance and Geometry Inference From an Unconstrained Image(SIGGRAPH2018, USC)
+- High-Fidelity Facial Reflectance and Geometry Inference From an Unconstrained Image(SIGGRAPH2018, USC)
 
-    
+     
 
 ### Texture
 
 > 3D-aid texture generation/ UV texture completion  
 > Keys: GAN
 
-1. Face Synthesis from Facial Identity Features(CVPR2017, google)
+- Face Synthesis from Facial Identity Features(CVPR2017, google)
 
    `3dmm, dl, landmarks`
 
-2. Photorealistic Facial Texture Inference Using Deep Neural Networks(CVPR2017, Hao Li, USC)
+- Photorealistic Facial Texture Inference Using Deep Neural Networks(CVPR2017, Hao Li, USC)
 
    `texture completion`
 
-3. UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition(CVPR2018, SZ, ICL)
+- UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition(CVPR2018, SZ, ICL)
 
    `gan, 3dmm, uv texture completion`
 
-4. Multi-Attribute Robust Component Analysis for Facial UV Maps(2017, SZ, ICL)
+- Multi-Attribute Robust Component Analysis for Facial UV Maps(2017, SZ, ICL)
 
-5. Realistic Dynamic Facial Textures from a Single Image using GANs(CVPR2017, Hao Li, USC, DeepMind)
+- Realistic Dynamic Facial Textures from a Single Image using GANs(CVPR2017, Hao Li, USC, DeepMind)
 
-6. Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model(2018)
+- Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model(2018)
 
-7. Side Information for Face Completion: a Robust PCA Approach(20180120, SZ, ICL)
+- Side Information for Face Completion: a Robust PCA Approach(20180120, SZ, ICL)
 
   ​    
 
 ### Transfer&Reenactment(Applications)
 
-1. Face Transfer with Multilinear Models (SIGGRAPH2005)
+- Face Transfer with Multilinear Models (SIGGRAPH2005)
 
    `Cartesian product(ID x EX x VI)`
 
-2. Online Modeling For Realtime Facial Animation(TOG2013)
+- Online Modeling For Realtime Facial Animation(TOG2013)
 
    `rgbd, blendshape, corrective field `
 
-3. Displaced Dynamic Expression Regression for Real-time Facial Tracking and Animation(SIGGRAPH2014)
+- Displaced Dynamic Expression Regression for Real-time Facial Tracking and Animation(SIGGRAPH2014)
 
-4. Real-time Expression Transfer for Facial Reenactment(SIGGRAPH AISA 2015)
+- Real-time Expression Transfer for Facial Reenactment(SIGGRAPH AISA 2015)
 
-5. Face2Face: Real-time Face Capture and Reenactment of RGB Videos(CVPR2016)
+- Face2Face: Real-time Face Capture and Reenactment of RGB Videos(CVPR2016)
 
    `capture, transfer, 3dmm, landmarks, texture, expression, mouth retrieval `
 
-6. Synthesizing Obama: Learning Lip Sync from Audio(SIGGRAPH2017)
+- Synthesizing Obama: Learning Lip Sync from Audio(SIGGRAPH2017)
 
-7. Deep Video Portrait(SIGGRAPH2018)
+- Deep Video Portrait(SIGGRAPH2018)
 
-8. HeadOn: Real-time Reenactment of Human Portrait Videos(SIGGRAPH2018)
+- HeadOn: Real-time Reenactment of Human Portrait Videos(SIGGRAPH2018)
 
 
 
 ### 3D-aid 2D face recognition
 
-1. Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification(ECCV2012, Columbia University)
+- Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification(ECCV2012, Columbia University)
 
-2. Face Recognition from a Single Training Image under Arbitrary Unknown Lighting Using Spherical Harmonics(PAMI2006)
+- Face Recognition from a Single Training Image under Arbitrary Unknown Lighting Using Spherical Harmonics(PAMI2006)
 
-3. 3D-aided face recognition robust to expression and pose variations (CVPR2014)
+- 3D-aided face recognition robust to expression and pose variations (CVPR2014)
 
-4. Effective 3D based Frontalization for Unconstrained Face Recognition(ICPR2016, MICC, Florence)
+- Effective 3D based Frontalization for Unconstrained Face Recognition(ICPR2016, MICC, Florence)
 
-5. Effective Face Frontalization in Unconstrained Images(CVPR2015, TH, Israel)
+- Effective Face Frontalization in Unconstrained Images(CVPR2015, TH, Israel)
 
-6. Do We Really Need to Collect Millions of Faces for Effective Face Recognition(ECCV2016, TH, USC, Israel)
+- Do We Really Need to Collect Millions of Faces for Effective Face Recognition(ECCV2016, TH, USC, Israel)
 
-7. High-Fidelity Pose and Expression Normalization for Face Recognition in the Wild(CVPR2015)
+- High-Fidelity Pose and Expression Normalization for Face Recognition in the Wild(CVPR2015)
 
-8. When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition(2017)
+- When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition(2017)
 
-9. Towards Large-Pose Face Frontalization in the Wild    
+- Towards Large-Pose Face Frontalization in the Wild    
+
+- Fully Automatic Pose-Invariant Face Recognition via 3D Pose Normalization   (ICCV2011, Cambridge, MA, USA)
 
-10. Fully Automatic Pose-Invariant Face Recognition via 3D Pose Normalization   (ICCV2011, Cambridge, MA, USA)
 
 
-   
 
 ### 3D face recognition
 
-1. Face Identification across Different Poses and Illuminations with a 3D Morphable Model(Automatic Face and Gesture Recognition2002, VB&TV)
+- Face Identification across Different Poses and Illuminations with a 3D Morphable Model(Automatic Face and Gesture Recognition2002, VB&TV)
 
-2. Preliminary Face Recognition Grand Challenge Results(2006)
+- Preliminary Face Recognition Grand Challenge Results(2006)
 
-3. expression Invariant 3D Face Recognition with a Morphable Model(FG2008, TV, Basel)
+- expression Invariant 3D Face Recognition with a Morphable Model(FG2008, TV, Basel)
 
-4. Bosphorus Database for 3D Face Analysis(2008)[data]()
+- Bosphorus Database for 3D Face Analysis(2008)[data]()
 
-5. Robust Learning from Normals for 3D face recognition(ECCV2012, SZ, ICL)
+- Robust Learning from Normals for 3D face recognition(ECCV2012, SZ, ICL)
 
-6. Static and dynamic 3D facial expression recognition: A comprehensive survey(IVC2012, SZ, LijunYin)
+- Static and dynamic 3D facial expression recognition: A comprehensive survey(IVC2012, SZ, LijunYin)
 
-7. Deep 3D Face Identification(2017, USC)
+- Deep 3D Face Identification(2017, USC)
 
-8. Robust Face Recognition with Deeply Normalized Depth Images (2018)
+- Robust Face Recognition with Deeply Normalized Depth Images (2018)
 
    `depth image(front&neural)`
 
-9. Learning from Millions of 3D Scans for Large-scale 3D Face Recognition(CVPR2018, Western Australia) 
+- Learning from Millions of 3D Scans for Large-scale 3D Face Recognition(CVPR2018, Western Australia) 
 

README.md

@@ -2,7 +2,18 @@
 
 ## Introduction
 
-Hope this helps & you enjoy it.  ^_^
+This project implements some basic functions related to 3D faces.
+
+You can use this to process mesh data, generate 3D faces from morphable model, reconstruct 3D face with a single image and key points as inputs, render faces with difference lightings(for more, please see examples).
+
+In the beginning, I wrote this project for learning 3D face reconstruction and for personal research use, so all the codes are written in python(numpy). However, some functions(eg. rasterization) can not use vectorization to optimize, writing them in python is too slow to use, then I choose to write these core parts in c++(without any other big libraries, such as opencv, eigen) and compile them with Cython for python use. 
+So the final version is very lightweight and fast.
+
+In addition, the numpy version is also retained, considering that beginners can focus on algorithms themselves in python and researches can modify and verify their ideas quickly. I also try my best to add references/formulas in each function, so that you can learn basic knowledge and understand the codes. 
+
+For more information and researches related to 3D faces, please see [3D face papers](https://github.com/YadiraF/face3d/blob/master/3D Face Papers.md).
+
+Enjoy it ^_^ 
 
 
 
@@ -11,28 +22,26 @@ Hope this helps & you enjoy it.  ^_^
 ```python
 # Since triangle mesh is the most popular representation of 3D face, 
 # the main part is mesh processing.
-mesh/             # written in python(numpy)
+mesh/             # written in python and c++
+|  cython/               # c++ files, use cython to compile 
 |  io.py                 # read & write obj
 |  vis.py                # plot mesh
 |  transform.py          # transform mesh & estimate matrix
 |  light.py              # add light & estimate light(to do)
 |  render.py             # obj to image using rasterization render
 
-# When can not use vercorization to optimize, loops in python are too slow to use. 
-# So write core parts(loops) in c++, then use cython to compile them for python api.
-mesh_cython/      # written in c++(cython)
-|  render.py             # the same API as in mesh/render.py, with faster speed.
-|  light.py              # the same API as in mesh/light.py, with faster speed.
-|  mesh_core*            # c++ codes of core parts.
+mesh_numpy/      # the same with mesh/, with each part written in numpy
+                 # slow but easy to learn and modify
 
-# 3DMM is one of the most popular methods to generate & reconstruct 3d face.
-morphabel_model/  # witten in python(numpy)
+# 3DMM is one of the most popular methods to generate & reconstruct 3D face.
+morphable_model/
 |  morphable_model.py    # morphable model class: generate & fit
 |  fit.py                # estimate shape&expression parameters. 3dmm fitting.
 |  load.py               # load 3dmm data
 ```
 
 
+
 ## Examples:
 
 `cd ./examples`
@@ -112,7 +121,7 @@ morphabel_model/  # witten in python(numpy)
 2. Compile c++ files to .so for python use (ignore if you use numpy version)
 
     ```bash
-    cd face3d/mesh_cython
+    cd face3d/mesh/cython
     python setup.py build_ext -i 
     ```
 
@@ -122,12 +131,21 @@ morphabel_model/  # witten in python(numpy)
 
 4. Run examples
 
-    (examples use cython version, you can change `mesh_cython` into `mesh` to use numpy version)
+    (examples use cython version, you can change `mesh` into `mesh_numpy` to use numpy version)
 
     ```bash
     cd examples
     python 1_pipeline.py 
     ```
 
-   
+For beginners who want to continue researches on 3D faces, I strongly recommend you first run examples according to the order, then view the codes in mesh_numpy and read the comments written in the beginning in each file. Hope this helps!  
+
+Moreover, I am new in computer graphics, so it would be great appreciated if you could point out some of my wrong expressions. Thanks!
+
+
+
+## Changelog
+
+* 2018/10/08 change structure. add comments. add introduction. add paper collections. 
+* 2018/07/15 first release