This step is the same as smplx.
To download the SMPL model go to this (male and female models, version 1.0.0, 10 shape PCs) and this (gender neutral model) project website and register to get access to the downloads section.
To download the SMPL+H model go to this project website and register to get access to the downloads section.
To download the SMPL-X model go to this project website and register to get access to the downloads section.
Place them as following:
data
└── smplx
├── J_regressor_body25.npy
├── J_regressor_body25_smplh.txt
├── J_regressor_body25_smplx.txt
├── J_regressor_mano_LEFT.txt
├── J_regressor_mano_RIGHT.txt
├── smpl
│ ├── SMPL_FEMALE.pkl
│ ├── SMPL_MALE.pkl
│ └── SMPL_NEUTRAL.pkl
├── smplh
│ ├── MANO_LEFT.pkl
│ ├── MANO_RIGHT.pkl
│ ├── SMPLH_FEMALE.pkl
│ └── SMPLH_MALE.pkl
└── smplx
├── SMPLX_FEMALE.pkl
├── SMPLX_MALE.pkl
└── SMPLX_NEUTRAL.pklThis part is used in 1v1p*.py. You can skip this step if you only use the multiple views dataset.
Download pretrained SPIN model here and place it to data/models/spin_checkpoints.pt.
Fetch the extra data here and place the smpl_mean_params.npz to data/models/smpl_mean_params.npz.
You can skip this step if you use openpose as your human keypoints detector.
Download yolov4.weights and place it into data/models/yolov4.weights.
Download pretrained HRNet weight and place it into data/models/pose_hrnet_w48_384x288.pth.
data
└── models
├── smpl_mean_params.npz
├── spin_checkpoint.pt
├── pose_hrnet_w48_384x288.pth
└── yolov4.weights - python>=3.6
- torch==1.4.0
- torchvision==0.5.0
- opencv-python
- pyrender: for visualization, or pyrender for server without a screen.
- chumpy: for loading SMPL model
- OpenPose[4]: for 2D pose
Some of python libraries can be found in requirements.txt. You can test different version of PyTorch.
python3 setup.py develop --user