The blender toolkit for REal 3D from REconstruction with Synthetic Humans (RefRESH).

Video Example of the Created Data | Project Page | Blog | Learning Rigidity Repository

If you use this code or our generated dataset, please cite the following paper:

Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation, Zhaoyang Lv, Kihwan Kim, Alejandro Troccoli, Deqing Sun, James M. Rehg, Jan Kautz, European Conference on Computer Vision 2018

@inproceedings{Lv18eccv,  
  title     = {Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation},  
  author    = {Lv, Zhaoyang and Kim, Kihwan and Troccoli, Alejandro and Rehg, James and Kautz, Jan},  
  booktitle = {ECCV},  
  year      = {2018}  
}

The inference algorithm and relevant networks are located in the Learning Rigidity repository:

git clone https://github.com/NVlabs/learningrigidity

Zhaoyang Lv: [email protected]

• Dependencies
• Prepare 3D static data
• Prepare 3D dynamic data
• Run Blender Instructions
• License
• Download existing data (Soon)
• Acknowledgement

• We currently only support loading BundleFusion RGB-D sequences and meshes. Please be aware that loading different sequences and meshes may result in a change in data loading protocol. Feel free to contribute.

• All codes are tested by Zhaoyang on linux machines and servers. For any other systems, there is no guarantee how to run it and I have no devices to reproduce the relevant issues.

• Although the rendering code is automatically batch run as we wish, it can still be slow on a single machine. Please consider use a server with multiple CPU cores to do that.

• There are some small changes after the paper submission to make the rendering process much easier to run and we change the rendering output to the multichannel OpenEXR format. In this way we also generate two pass flow, rather than one in the original data. Please let me know if it generates unexpected results.

• Currently there is no strict roadmap about how to extend this toolkit, although I might add functionalities to support different foreground objects, more background meshes, only if there is a research need.

We need blender which has OSL support for rendering (Do not use ubuntu default blender (2.76), which does not have full support for Open Shading Language). Download the blender (2.79 version we tested) and set the blender path

BLENDER_PYTHON=~/develop/blender-2.79b/2.79/python
alias blender_python=$BLENDER_PYTHON/bin/python3.5m

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
$BLENDER_PYTHON/bin/python3.5m get-pip.py

alias blender_pip=$BLENDER_PYTHON/bin/pip3
# install the python dependencies for blender python  
blender_pip install -r setup/blender_requirements.txt

sudo apt-get install openexr
sudo apt-get install libopenexr-dev

We have already created the conda environment for all the dependencies:

conda env create -f setup/conda_environment.yml

Our dataset creation strategy is not limited to any particular 3D reconstruction dataset. In this paper, we use the scenes reconstructed from BundleFusion project only as an example.

Create a symbolic link for all the target files in the data folder named RefRESH:

mkdir data
ln -s $BUNDLE_FUSION data/RefFRESH

Download all the reconstructed scenes and source files from their website:

# Change the destinationn in the script file if you want to use a different location.
sh setup/download_BundleFusion.sh

Run the bundlefusion utility script to generate the bundle fusion pickle files.

python miscs/process_bundlefusion.py

Similarly, we will also generate the pickle files for all relevant data so that we can more easily accesss in all different projects.

In the smpl_data/README.md, you should finally see all the items in the list.

You need to download SMPL for MAYA from the official website(click here) 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:

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 smpl_data folder.

With the same credentials as with the SURREAL dataset and within the smpl_data folder, you can download the remaining necessary SMPL data. All the downloaded files should be placed within the same directory:

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

For a more detailed instructions specifically for this dataset, please refer to smpl_data/README.md.

To create your dataset with the blender toolkit, please refer to ./blender/README

The dataset created in the paper for pretraining is available. Please check the dataset readme for several notes for it.

MIT License

Copyright (c) 2018 Zhaoyang Lv

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Our work can not be done without the precedent research efforts. Part of the human rendering code is refactored on top of the SURREAL toolkit.