belinghy / smpl-ik

SMPL-IK

Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows https://arxiv.org/pdf/2208.08274.pdf

Create workspace and clone this repository

mkdir workspace

cd workspace

git clone [e-mail]:boreshkinai/smpl-ik.git

Setup : Docker

docker build -f Dockerfile -t smpl-ik:$USER .

nvidia-docker run -p 18888:8888 -p 16006:6006 -v ~/workspace/smpl-ik:/workspace/smpl-ik -t -d --shm-size="1g" --name smpl-ik_$USER smpl-ik:$USER

go inside docker container

docker exec -i -t smpl-ik_$USER  /bin/bash 

launch training session

python run.py --config=protores/configs/experiments/h36m/protores_h36m.yaml

Setup : Conda

conda create --name protores python=3.8

conda activate protores 

pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html

To use notebooks (optional) :

conda install jupyter

Citation

If you use ProtRes in any context, please cite the following paper:

@inproceedings{oreshkin2022protores:,
  title={ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics},
  author={Boris N. Oreshkin and Florent Bocquelet and F{\'{e}}lix G. Harvey and Bay Raitt and Dominic Laflamme},
  booktitle={International Conference on Learning Representations},
  year={2022}
}