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# Human Mesh Recovery
## Data
1. Download the datasets [here](https://1drv.ms/f/s!AvAdh0LSjEOlfy-hqlHxdVMZxWM) and put them to `data/mesh/`. We use Human3.6M, COCO, and PW3D for training and testing. Descriptions of the joint regressors could be found in [SPIN](https://github.com/nkolot/SPIN/tree/master/data).
2. Download the SMPL model(`basicModel_neutral_lbs_10_207_0_v1.0.0.pkl`) from [SMPLify](https://smplify.is.tue.mpg.de/), put it to `data/mesh/`, and rename it as `SMPL_NEUTRAL.pkl`
## Running
**Train from scratch:**
```bash
# with 3DPW
python train_mesh.py \
--config configs/mesh/MB_train_pw3d.yaml \
--checkpoint checkpoint/mesh/MB_train_pw3d
# H36M
python train_mesh.py \
--config configs/mesh/MB_train_h36m.yaml \
--checkpoint checkpoint/mesh/MB_train_h36m
```
**Finetune from a pretrained model:**
```bash
# with 3DPW
python train_mesh.py \
--config configs/mesh/MB_ft_pw3d.yaml \
--pretrained checkpoint/pretrain/MB_release \
--checkpoint checkpoint/mesh/FT_MB_release_MB_ft_pw3d
# H36M
python train_mesh.py \
--config configs/mesh/MB_ft_h36m.yaml \
--pretrained checkpoint/pretrain/MB_release \
--checkpoint checkpoint/mesh/FT_MB_release_MB_ft_h36m
```
**Evaluate:**
```bash
# with 3DPW
python train_mesh.py \
--config configs/mesh/MB_train_pw3d.yaml \
--evaluate checkpoint/mesh/MB_train_pw3d/best_epoch.bin
# H36M
python train_mesh.py \
--config configs/mesh/MB_train_h36m.yaml \
--evaluate checkpoint/mesh/MB_train_h36m/best_epoch.bin
```
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