EasyMocap - Installation

0. Download models

0.1 SMPL models

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.pkl

0.2 (Optional) SPIN model

This 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.

0.3 (Optional) 2D model

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

2. Requirements

python>=3.6
torch==1.4.0
torchvision==0.5.0
opencv-python
pyrender: for visualization
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.

3. Install

python3 setup.py develop --user

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