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.pyto set up the paths. tensorflowortensorflow-gpuin 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
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
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 .
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.
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}
}
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.