cogito2012 / 3dfacerecon Goto Github PK

This repo is the re-implementation work of the paper Learning Detailed Face Reconstruction from a Single Image (CVPR2017).

• 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.

1. Download the images URL files from VGGFace. 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:

   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, where the following .mat files are needed in the ./3dmm folder:

   01_MorphableModel.mat
   Model_Expression.mat
   model_info.mat
   vertex_code.mat

   You can also download them from Baidu Cloud or Google Drive.

   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

   # 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
   

1. Compiling the custom op with tensorflow.

   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.

   cd ./rendering_layer
   python sample_test.py
   cd ..

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

   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:

   cd ./output/tensorboard
   tensorboard --logdir=./ --port=6710

• 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.

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}
}