SMPLify-XMC
CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.
This repo is part of our project: On Self-Contact and Human Pose.
[Project Page] [Paper] [MPI Project Page]
Description and Demo
SMPLify-XMC adapts SMPLify-X to fit SMPL-X model to Mimic The Pose (MTP) data. To run SMPLify-XMC you need
- an image of a person mimicking a presented pose
- the presented pose parameters
- the person's gender, height and weight
- the OpenPose keypoints.
The code has been tested with Python 3.6.9, CUDA 10.1, CuDNN 7.5 and PyTorch 1.8.1 on Ubuntu 18.04.
Installation
1) Clone this repo
git clone [email protected]:muelea/smplify-xmc.git
cd smplify-xmc
2) Download body model
Download them SMPL-X body model from https://smpl-x.is.tue.mpg.de and save it in MODEL_FOLDER. You can replace model_folder: MODEL_FOLDER in the config file configs/fit_smplx_singleview.yaml or use an environment variable.
3) Download essentials
Download essentials from here and unpack to ESSENTIALS_DIR. Then create symlinks between the essentials and this repo:
ln -s $ESSENTIALS_DIR/smplify-xmc-essentials data/essentials
4) Create python virtual environment
python3 -m venv $YOUR_VENV_DIR/smplify-xmc
source $YOUR_VENV_DIR/smplify-xmc/bin/activate
5) Install requirements
pip install -r requirements.txt
6) Get dependencies
Clone self-contact repo, e.g. to YOUR_PYTHON_PACKAGE_DIR. Then use pip to install the package. Then you can import the self-contact functions from any place in your system. (make sure your venv is activated).
cd $YOUR_PYTHON_PACKAGE_DIR
git clone [email protected]:muelea/selfcontact.git
cd selfcontact
rm -r .git
pip install .
cd ..
Demo using our example data
You can find our example dataset in this repo under data/example_input. The following command will automatically save parameters, mesh, and image under output_dir:
python main_singleview.py --config configs/fit_smplx_singleview.yaml \
--dataset mtp_demo \
--input_base_dir data/example_input/singleview/subject1 \
--input_dir_poses data/example_input/presented_poses \
--output_dir data/example_output/singleview/subject1 \
--model_folder $MODELS_FOLDER
Process the MTP dataset:
Download MTP data from the TUCH website: https://tuch.is.tue.mpg.de and save the data in DS_DIR. You should now see a folder named $DS_DIR/mtp.
Read MTP data: python lib/dataextra/preprocess_mtp_mturk_dataset.py --ds_dir=$DS_DIR/mtp
Process the first item: python main_singleview.py --config configs/fit_smplx_singleview_mtp_dataset.yaml --db_file data/dbs/mtp_mturk.npz --output_dir data/example_output/mtp/ --model_folder=$MODEL_FOLDER --cluster_bs=1 --ds_start_idx=0
Process your own data:
Follow the structure of the example data in data/example_input. Create a folder PP_FOLDER for the presented poses:
PP_FOLDER
----pose_name1.pkl
----pose_name2.pkl
The pickle file should contain a dictionary with the pose parameters and the vertices. If you include the vertices ('v'), the vertices in contact will be computed automatically.
data = {
'body_pose': ..
'right_hand_pose': ..
'left_hand_pose': ..
'global_orient': ..
'v': .. #vertices
}
Then create a folder MI_FOLDER for the mimicked images, following the structure below. Compute the keypoints for each image from OpenPose. The meta file should contain the gender, height and weight of the subject mimicking the pose.
MI_FOLDER
----subject_name1
----images
----pose_name1.png
----pose_name2.png
----keypoints
----pose_name1.json
----pose_name2.json
----meta.yaml
Finally, run the fitting code:
python main_singleview.py --config configs/fit_smplx_singleview.yaml \
--input_base_dir $MI_FOLDER/subject_name1 \
--input_dir_poses $PP_FOLDER \
--output_dir data/example_output/subject_name1
Citation
@inproceedings{Mueller:CVPR:2021,
title = {On Self-Contact and Human Pose},
author = {M{\"u}ller, Lea and Osman, Ahmed A. A. and Tang, Siyu and Huang, Chun-Hao P. and Black, Michael J.},
booktitle = {Proceedings IEEE/CVF Conf.~on Computer Vision and Pattern Recogßnition (CVPR)},
month = jun,
year = {2021},
doi = {},
month_numeric = {6}
}
Acknowledgement
We thank Vassilis Choutas and Georgios Pavlakos for publishing the SMPLify-X code: https://github.com/vchoutas/smplify-x. This has allowed us to build our code on top of it and continue to use important features, such as the prior or optimization. Again, special thanks to Vassilis Choutas for his implementation of the generalized winding numbers and the measurements code. We also thank our data capture and admin team for their help with the extensive data collection on Mechanical Turk and in the Capture Hall. Many thanks to all subjects who contributed to this dataset in the scanner and on the Internet. Thanks to all PS members who proofread the script and did not understand it and the reviewers, who helped improving during the rebuttal. Lea Mueller and Ahmed A. A. Osman thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting them. We thank the wonderful PS department for their questions and support.
