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3DFaceRecon

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Re-implementation: "[CVPR 2017] Learning Detailed Face Reconstruction from a Single Image".

// repository documentation

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## Re-implementation Work of 3D Face Reconstruction

This repo is the re-implementation work of the paper [Learning Detailed Face Reconstruction from a Single Image (CVPR2017)](http://openaccess.thecvf.com/content_cvpr_2017/papers/Richardson_Learning_Detailed_Face_CVPR_2017_paper.pdf).

![results](https://raw.githubusercontent.com/Cogito2012/3DFaceRecon/master/paper_info/result_im.png)
![framework](https://raw.githubusercontent.com/Cogito2012/3DFaceRecon/master/paper_info/framework.png)

### Requirement

> * python == 3.5.x (with opencv-python, scipy, etc. Anaconda==3.4.x is recommended.)
> * TensorFlow == 1.2.0 (with CUDA8.0 and cudnn5.1).
> * gcc == 4.8.x (for compiling rendering layer).
> * Currently, the codes are tested only on the Linux platform with environment above.

### Data Preparation

1. Download the images URL files from [VGGFace](http://www.robots.ox.ac.uk/~vgg/data/vgg_face/). Then extract them into the `./data` folder.

To download all the images of VGGFace, you can use the `download_vggface.py` in the folder `./data`:
	```shell
	cd ./data
	python download_vggface.py ./vgg_face_dataset/files  # This process may take long time.
	```

2. Compile the Zbuffer Lib within the folder `./prepare_data/ZBuffer` by running the script: `compile.m`

**Note**: you may need to setup mex compiler and modify the directories of opencv accordingly if errors occured.

You can also run the MATLAB script `./test_zbuffer.m` to test the generated lib file, i.e., `ZBufferC.mexa64`.

3. Prepare 3DMM facial model.

You should firstly download the basic BFM model from [3DDFA](http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm), where the following `.mat` files are needed in the `./3dmm` folder:    
	```shell
	01_MorphableModel.mat
	Model_Expression.mat
	model_info.mat
	vertex_code.mat
	```

You can also download them from [Baidu Cloud](https://pan.baidu.com/s/1S_mJMFUxc_9FSw4eMGdVKw) or [Google Drive](https://drive.google.com/drive/folders/1b0m7W9SwwZyHqk4XnzEFjdvvsvJbL_TO?usp=sharing).

Then, run the MATLAB script `./prepare_data/script_ModelGenerate.m` to generate `Model_Shape.mat` file in the `./3dmm` folder.

4. Ground truth generation. The matlab codes `./prepare_data/script_generate_dataset.m` are used for generating training data. You should modify the input and output directories accordingly. As a result, facial images and text labels should be placed in folder `./data/vggface`.

Only the cropped facial images and text labels are used in this project. The 235-dimensional param vecters are saved in the `labels` folder.    
> * dim 1-7: pose parameters ([phi; gamma; theta; t3d_x; t3d_y; t3d_z; f];)    
> * dim 7+1 --> 7+199: shape parameters.    
> * dim 7+199+1 --> 7+199+29: expression parameters.

5. Prepare training dataset. Just run the following python scripts

```shell
	# split training dataset into train val and test splits.
	cd ./prepare_data
	python script_split_dataset.py
	# compute the mean value for training.
	python script_compute_mean.py

```

### Installation

1. Compiling the custom op with tensorflow.    
	```shell
	cd ./rendering_layer
	sh ./compile.sh
	cd ..

```
    Make sure no errors happened in this compiling process and the file `render_depth.so` is generated under the folder `./rendering_layer/ops_src/`

2. Simple Test. You can use the provided python script `sample_test.py` to test the `render_depth.so`.   
	```shell
	cd ./rendering_layer
	python sample_test.py
	cd ..
	```

### Train and Test

1. Download the pretrained ImageNet model files, i.e., `resnet_v1_101.ckpt` and `vgg_16.ckpt`.

```shell
	cd ./pretrained
	sh download_imagenet_models.sh
	```

2. For training, just run the shell script `./run_experiment.sh` directly, or you can modify several input args before running.

To visualize the training process, you can use the tensorboard tool:  
	
	```shell
	cd ./output/tensorboard
	tensorboard --logdir=./ --port=6710
	```

### Disclaimer

> * Currently, this repo has not been well improved to completely get exactly the same results of the paper.
> * Parts of the C++ codes related to the zbuffer rendering are referenced from [3DDFA](http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm).

### Citation
If you find this implementation helpful to your research paper, please consider citing:

@article{Richardson_CVPR2017,
	    Author = {E. Richardson and M. Sela and R. Or-El and R. Kimmel},
	    Title = {Learning Detailed Face Reconstruction from a Single Image},
	    booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	    Year = {2017}
	}

3DFaceRecon

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