Neural Body Fitting code repository

Setup:

• git clone --recursive http://github.com/mohomran/neural_body_fitting
• create and activate a fresh virtualenv
• pip install tensorflow-gpu==1.6.0 (or tensorflow==1.6.0)
• inside the root folder run pip install -r requirements.txt
• navigate to external/up and run python setup.py develop (which will install the UP toolbox)
• download SMPL (at http://smpl.is.tue.mpg.de/downloads) and unzip to external/
• download the segmentation model and extract into models/
• download the fitting model and extract into experiments/states

Demo:

The following command will perform inference on 60 images from the UP dataset:

python run.py infer_segment_fit experiments/config/demo_up/ \
              --inp_fp demo/up/input/\
              --out_fp demo/up/output\
              --visualise render

The results can be viewed by opening the file demo/up/output/index.html in a browser. These were selected to demonstrate both success and failure cases. Most of the processing time (~80%) is taken up by the mesh renderer. Alternatively, you can use --visualise pose which is quicker and just plots the projected SMPL joints.

Training:

Coming Soon

Citation

If you find any parts of this code useful, please cite the following paper:

@inproceedings {omran2018nbf,
  title = {Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation},
  journal = {International Conference on 3D Vision (3DV)},
  year = {2018},
  author = {Omran, Mohamed and Lassner, Christoph and Pons-Moll, Gerard and Gehler, Peter V. and Schiele, Bernt}
  address = {Verona, Italy},
}

Acknowledgements

The repository is modelled after (and partially adopts code from) Christoph Lassner's Generating People project. The example data provided is from his Unite the People dataset.