This is the code adopted from the following paper:

Gül Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J. Black, Ivan Laptev and Cordelia Schmid, Learning from Synthetic Humans, CVPR 2017.

Check the project page for more materials.

Contact: Gül Varol.

• 1. Create your own synthetic data
• Citation
• License
• Acknowledgements

a) You need to download SMPL for MAYA from http://smpl.is.tue.mpg.de in order to run the synthetic data generation code. Once you agree on SMPL license terms and have access to downloads, you will have the following two files:

This contains the basic smpl model, including the articulated shape, and smpl blending shapes.

basicModel_f_lbs_10_207_0_v1.0.2.fbx
basicModel_m_lbs_10_207_0_v1.0.2.fbx

Place these two files under datageneration/smpl_data folder.

b) With the same credentials as with the SURREAL dataset, you can download the remaining necessary SMPL data and place it in datageneration/smpl_data. This includes the motion capture data (MoCap), including the pelvis position (trans) and the poses (poses) for a list of sequences. There are also regression_verts, and joint_regressor to build the connection with joints. Another important information is the shapes (maleshapes, femaleshapes) from real persons. regression_verts denotes the bones or key vertice indices, while joint_regressor is used to predict the joint position from given vertices.

./download_smpl_data.sh /path/to/smpl_data yourusername yourpassword

smpl_data/
------------- textures/ # folder containing clothing images (also available at lsh.paris.inria.fr/SURREAL/smpl_data/textures.tar.gz)
------------- (fe)male_beta_stds.npy
------------- smpl_data.npz # 2.5GB
 # trans*           [T x 3]     - (T: number of frames in MoCap sequence)
 # pose*            [T x 72]    - SMPL pose parameters (T: number of frames in MoCap sequence)
 # maleshapes       [1700 x 10] - SMPL shape parameters for 1700 male scans
 # femaleshapes     [2103 x 10] - SMPL shape parameters for 2103 female scans 
 # regression_verts [232]
 # joint_regressor  [24 x 232]

Note: SMPL pose parameters are MoSh'ed from CMU MoCap data. Note that these are not the most recent MoSh results. For any questions regarding MoSh, please contact mosh@tue.mpg.de instead. Here, we only provide the pose parameters for MoCap sequences, not their shape parameters (they are not used in this work, we randomly sample body shapes).

We only provide names of the background images we used. They are downloaded from LSUN dataset using this code. You can download images from this dataset or use any other images.

You need to download Blender and install scipy package to run the first part of the code. The provided code was tested with Blender2.78, which is shipped with its own python executable as well as distutils package. Therefore, it is sufficient to do the following:

# Install pip
/blenderpath/2.78/python/bin/python3.5m get-pip.py
# Install scipy
/blenderpath/2.78/python/bin/python3.5m pip install scipy

get-pip.py is downloaded from pip. Replace the blenderpath with your own and set BLENDER_PATH.

Otherwise, you might need to point to your system installation of python, but be prepared for unexpected surprises due to version mismatches. There may not be support about questions regarding this installation.

If you want to save the rendered images as videos, you will need ffmpeg library. Build it and set the FFMPEG_PATH to the directory that contains lib/ and bin/ folders. Additionally, if you want to use H.264 codec as it is done in the current version of the code, you need to have the x264 libraries compiled. In that case, set X264_PATH to your build. If you use another codec, you don't need X264_PATH variable and you can remove -c:v h264 from main_part1.py.

This is how the ffmpeg was built:

# x264
./configure  --prefix=/home/gvarol/tools/ffmpeg/x264_build --enable-static --enable-shared --disable-asm
make 
make install

# ffmpeg
./configure --prefix=/home/gvarol/tools/ffmpeg/ffmpeg_build_sequoia_h264 --enable-avresample --enable-pic --disable-doc --disable-static --enable-shared --enable-gpl --enable-nonfree --enable-postproc --enable-x11grab --disable-yasm --enable-libx264 --extra-ldflags="-I/home/gvarol/tools/ffmpeg/x264_build/include -L/home/gvarol/tools/ffmpeg/x264_build/lib" --extra-cflags="-I/home/gvarol/tools/ffmpeg/x264_build/include"
make
make install

The file type for some of the temporary outputs from Blender will be EXR images. In order to read these images, the code uses OpenEXR bindings for Python. These bindings are available for python 2, the second part of the code (main_part2.py) needs this library.

Copy the config.copy into config and edit the bg_path, tmp_path, output_path and openexr_py2_path with your own paths.

• bg_path contains background images and two files train_img.txt and test_img.txt. The ones used for SURREAL dataset can be found in datageneration/misc/LSUN. Note that the folder structure is flattened for each room type.

• tmp_path stores temporary outputs and is deleted afterwards. You can use this for debugging.

• output_path is the directory where we store all the final outputs of the rendering.

• openexr_py2_path is the path to libraries for OpenEXR bindings for Python.

run.sh script is ran for each clip. You need to set FFMPEG_PATH, X264_PATH (optional), PYTHON2_PATH, and BLENDER_PATH variables. -t 1 option can be removed to run on multi cores, it runs faster.

# When you are ready, type:
./run.sh

If you use this code, please cite the following:

@article{varol17a,
      TITLE = {Learning from Synthetic Humans},
      AUTHOR = {Varol, G{"u}l and Romero, Javier and Martin, Xavier and Mahmood, Naureen and Black, Michael J. and Laptev, Ivan and Schmid, Cordelia},
      JOURNAL = {CVPR},
      YEAR = {2017}
}

Please check the license terms before downloading and/or using the code, the models and the data. http://www.di.ens.fr/willow/research/surreal/data/license.html

The data generation code is built by Javier Romero, Gul Varol and Xavier Martin.

The training code is written by Gul Varol and is largely built on the ImageNet training example https://github.com/soumith/imagenet-multiGPU.torch by Soumith Chintala.