We provide the config files for HMR: End-to-End Recovery of Human Shape and Pose.
@inproceedings{HMR,
author = {Angjoo Kanazawa and
Michael J. Black and
David W. Jacobs and
Jitendra Malik},
title = {End-to-End Recovery of Human Shape and Pose},
booktitle = {CVPR},
year = {2018}
}- SMPL v1.0 is used in our experiments.
- J_regressor_extra.npy
- J_regressor_h36m.npy
- smpl_mean_params.npz
Download the above resources and arrange them in the following file structure:
mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
├── body_models
│ ├── J_regressor_extra.npy
│ ├── J_regressor_h36m.npy
│ ├── smpl_mean_params.npz
│ └── smpl
│ ├── SMPL_FEMALE.pkl
│ ├── SMPL_MALE.pkl
│ └── SMPL_NEUTRAL.pkl
├── preprocessed_datasets
│ ├── cmu_mosh.npz
│ ├── coco_2014_train.npz
│ ├── h36m_mosh_train.npz
│ ├── lspet_train.npz
│ ├── lsp_train.npz
│ ├── mpi_inf_3dhp_train.npz
│ ├── mpii_train.npz
│ └── pw3d_test.npz
└── datasets
├── coco
├── h36m
├── lspet
├── lsp
├── mpi_inf_3dhp
├── mpii
└── pw3d
We evaluate HMR on 3DPW. Values are MPJPE/PA-MPJPE.
| Config | 3DPW | Download |
|---|---|---|
| resnet50_hmr_pw3d.py | 112.34 / 67.53 | model | log |