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Official code repository for the paper "A Generative Model for People in Clothing".
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generation Add tools for rendering people conditioned on pose and shape. Jan 25, 2018
gp_tools Initial. Jul 31, 2017
.gitignore Add tools for rendering people conditioned on pose and shape. Jan 25, 2018
LICENSE.txt
README.md
cm_22.png Initial. Jul 31, 2017
config.py
requirements.txt
setup.py Initial. Jul 31, 2017

README.md

Generating People code repository

Requirements:

  • OpenCV (on Ubuntu, e.g., install libopencv-dev and python-opencv).
  • SMPL (download at http://smpl.is.tue.mpg.de/downloads) and unzip to a place of your choice.
  • Edit the file config.py to set up the paths.
  • tensorflow or tensorflow-gpu in a version >=v1.1.0 (I did not want to add it to the requirements to force installation of the GPU or non-GPU version).
  • Only if you want to run pose estimation and 3D fitting to integrate new data into the dataset: set up the unite the people repository (https://github.com/classner/up) and adjust its path in config.py.

The rest of the requirements is then automatically installed when running:

python setup.py develop

Setting up the data

The scripts in generation/tools/ transform the Chictopia data to construct to the final database. Iteratively go through the scripts to create it. Otherwise, download the pre-processed data from our website (http://files.is.tuebingen.mpg.de/classner/gp/), unzip it to the folder generation/data/pose/extracted and only run the last script

./09_pack_db.sh full

Training / running models

Model configuration and training artifacts are in the experiments folder. The config subfolder contains model configurations (LSM=latent sketch module, CSM=conditional sketch module, PM=portray module, PSM_class=portray module with class input). You can track the contents of this folder with git since it's lightweight and no artifacts are stored there. To create a new model, just copy template (or link to the files in it) and change options.py in the new folder.

To run training/validation/testing use

./run.py [train,val,trainval,test,{sample}] experiments/config/modelname

where trainval runs a training on training+validation. Artifacts during training are written to experiments/states/modelname (you can run a tensorboard there for monitoring). The generated results from testing are stored in experiments/features/modelname/runstate, where runstate is either a training stage or point in time (if sampling). You can use the test_runner.py script to automatically scan for newly created training checkpoints and validating/testing them with the command

./test_runner.py experiments/states/modelname [val, test]

Pre-trained models can be downloaded from http://files.is.tuebingen.mpg.de/classner/gp .

Generating people

If you have trained or downloaded the LSM and PM models, you can use a convenience script to sample people. For this, navigate to the generation folder and run

./generate.sh n_people [out_folder]

to generate n_people to the optionally specified out_folder. If unspecified, the output folder is set to generated.

Citing

If you use this code for your research, please consider citing us:

@INPROCEEDINGS{Lassner:GeneratingPeople:2017,
  author    = {Christoph Lassner and Gerard Pons-Moll and Peter V. Gehler},
  title     = {A Generative Model for People in Clothing},
  year      = {2017},
  booktitle = {Proceedings of the IEEE International Conference on Computer Vision}
}

Acknowledgements

Our models are strongly inspired by the pix2pix line of work by Isola et al. (https://phillipi.github.io/pix2pix/). Parts of the code are inspired by the implementation by Christopher Hesse (https://affinelayer.com/pix2pix/). Overall, this repository is set up similar to the Deeplab project structure, enabling efficient model specification, tracking and training (http://liangchiehchen.com/projects/DeepLabv2_resnet.html) and combining it with the advantages of Tensorboard.

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