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VIBE

Introduction

We provide the config files for VIBE: VIBE: Video Inference for Human Body Pose and Shape Estimation.

@inproceedings{VIBE,
  author    = {Muhammed Kocabas and
               Nikos Athanasiou and
               Michael J. Black},
  title     = {{VIBE}: Video Inference for Human Body Pose and Shape Estimation},
  booktitle = {CVPR},
  year      = {2020}
}

Notes

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
    ├── pretrained
    │   └── spin.pth
    ├── preprocessed_datasets
    │   ├── vibe_mpi_inf_3dhp_train.npz
    │   └── vibe_insta_variety.npz
    └── datasets
        └── mpi_inf_3dhp

Results and Models

We evaluate VIBE on 3DPW. Values are MPJPE/PA-MPJPE.

Config 3DPW Download
resnet50_vibe_pw3d.py 94.89 / 57.08 model | log