HybrIK
A Hybrid Analytical-Neural Inverse Kinematics Solution, CVPR 2021
TODO
- [ ] Provide pretrained model
- [ ] Provide parsed data annotations
Installation instructions
# 1. Create a conda virtual environment.
conda create -n hybrik python=3.6 -y
conda activate hybrik
# 2. Install PyTorch
conda install pytorch==1.1.0 torchvision==0.3.0
# 3. Pull our code
git clone https://github.com/Jeff-sjtu/HybrIK.git
cd HybrIK
# 4. Install
python setup.py develop
Fetch data
Download Human3.6M, MPI-INF-3DHP, 3DPW and MSCOCO datasets. You need to follow directory structure of the data
as below.
|-- data
`-- |-- h36m
`-- |-- pw3d
`-- |-- 3dhp
`-- |-- coco
`-- |-- annotations
| |-- person_keypoints_train2017.json
| `-- person_keypoints_val2017.json
|-- train2017
`-- val2017
- Download Human3.6M parsed data. (WIP)
- Download 3DPW parsed data. (WIP)
- Download MPI-INF-3DHP parsed data. (WIP)
Train from scratch
./scripts/train_smpl.sh train_res34 ./configs/256x192_adam_lr1e-3-res34_smpl_3d_base_2x_mix.yaml
Evaluation
./scripts/validate_smpl.sh ./configs/256x192_adam_lr1e-3-res34_smpl_3d_base_2x_mix.yaml ${CKPT}
Citing
If our code helps your research, please consider citing the following paper:
@inproceedings{li2020hybrik,
title={HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation},
author={Li, Jiefeng and Xu, Chao and Chen, Zhicun and Bian, Siyuan and Yang, Lixin and Lu, Cewu},
booktitle={CVPR},
year={2021}
}