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Data preparation

Overview

Our data pipeline use HumanData structure for storing and loading. The proprocessed npz files can be obtained from raw data using our data converters, and the supported configs can be found here.

These are our supported converters and their respective dataset-name:

  • AgoraConverter (agora)

  • AmassConverter (amass)

  • CocoConverter (coco)

  • CocoHybrIKConverter (coco_hybrik)

  • CocoWholebodyConverter (coco_wholebody)

  • CrowdposeConverter (crowdpose)

  • EftConverter (eft)

  • GTAHumanConverter (gta_human)

  • H36mConverter (h36m_p1, h36m_p2)

  • H36mHybrIKConverter (h36m_hybrik)

  • H36mSpinConverter (h36m_spin)

  • InstaVibeConverter (instavariety_vibe)

  • LspExtendedConverter (lsp_extended)

  • LspConverter (lsp_original, lsp_dataset)

  • MpiiConverter (mpii)

  • MpiInf3dhpConverter (mpi_inf_3dhp)

  • MpiInf3dhpHybrIKConverter (mpi_inf_3dhp_hybrik)

  • PennActionConverter (penn_action)

  • PosetrackConverter (posetrack)

  • Pw3dConverter (pw3d)

  • Pw3dHybrIKConverter (pw3d_hybrik)

  • SurrealConverter (surreal)

  • SpinConverter (spin)

  • Up3dConverter (up3d)

Datasets for supported algorithms

For all algorithms, the root path for our datasets and output path for our preprocessed npz files are stored in data/datasets and data/preprocessed_datasets. As such, use this command with the listed dataset-names:

python tools/convert_datasets.py \
  --datasets <dataset-name> \
  --root_path data/datasets \
  --output_path data/preprocessed_datasets

For HMR training and testing, the following datasets are required:

Convert datasets with the following dataset-names:

coco, pw3d, mpii, mpi_inf_3dhp, lsp_original, lsp_extended, h36m

Alternatively, you may download the preprocessed files directly:

The preprocessed datasets should have this structure:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    ├── datasets
    └── preprocessed_datasets
        ├── coco_2014_train.npz
        ├── h36m_train.npz
        ├── lspet_train.npz
        ├── lsp_train.npz
        ├── mpi_inf_3dhp_train.npz
        ├── mpii_train.npz
        └── pw3d_test.npz

For SPIN training, the following datasets are required:

Convert datasets with the following dataset-names:

spin, h36m_spin

Alternatively, you may download the preprocessed files directly:

The preprocessed datasets should have this structure:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    ├── datasets
    └── preprocessed_datasets
        ├── spin_coco_2014_train.npz
        ├── spin_h36m_train.npz
        ├── spin_lsp_train.npz
        ├── spin_lspet_train.npz
        ├── spin_mpi_inf_3dhp_train.npz
        ├── spin_mpii_train.npz
        └── spin_pw3d_test.npz

For VIBE training and testing, the following datasets are required:

The data converters are currently not available.

Alternatively, you may download the preprocessed files directly:

The preprocessed datasets should have this structure:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    ├── datasets
    └── preprocessed_datasets
        ├── vibe_insta_variety.npz
        ├── vibe_mpi_inf_3dhp_train.npz
        └── vibe_pw3d_test.npz

For HYBRIK training and testing, the following datasets are required:

Convert datasets with the following dataset-names:

h36m_hybrik, pw3d_hybrik, mpi_inf_3dhp_hybrik, coco_hybrik

Alternatively, you may download the preprocessed files directly:

The preprocessed datasets should have this structure:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    ├── datasets
    └── preprocessed_datasets
        ├── hybriK_coco_2017_train.npz
        ├── hybrik_h36m_train.npz
        ├── hybrik_mpi_inf_3dhp_train.npz
        └── hybrik_pw3d_test.npz

Folder structure

AGORA

AGORA (CVPR'2021) 
@inproceedings{Patel:CVPR:2021,
 title = {{AGORA}: Avatars in Geography Optimized for Regression Analysis},
 author = {Patel, Priyanka and Huang, Chun-Hao P. and Tesch, Joachim and Hoffmann, David T. and Tripathi, Shashank and Black, Michael J.},
 booktitle = {Proceedings IEEE/CVF Conf.~on Computer Vision and Pattern Recognition ({CVPR})},
 month = jun,
 year = {2021},
 month_numeric = {6}
}

For AGORA, please download the dataset and place them in the folder structure below:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
   └── datasets
       └── agora
           ├── camera_dataframe # smplx annotations
           │   ├── train_0_withjv.pkl
           │   ├── validation_0_withjv.pkl
           │   └── ...
           ├── camera_dataframe_smpl # smpl annotations
           │   ├── train_0_withjv.pkl
           │   ├── validation_0_withjv.pkl
           │   └── ...
           ├── images
           │   ├── train
           │   │   ├── ag_trainset_3dpeople_bfh_archviz_5_10_cam00_00000_1280x720.png
           │   │   ├── ag_trainset_3dpeople_bfh_archviz_5_10_cam00_00001_1280x720.png
           │   │   └── ...
           │   ├── validation
           │   └── test
           ├── smpl_gt
           │   ├── trainset_3dpeople_adults_bfh
           │   │   ├── 10004_w_Amaya_0_0.mtl
           │   │   ├── 10004_w_Amaya_0_0.obj
           │   │   ├── 10004_w_Amaya_0_0.pkl
           │   │   └── ...
           │   └── ...
           └── smplx_gt
                   ├── 10004_w_Amaya_0_0.obj
                   ├── 10004_w_Amaya_0_0.pkl
                   └── ...

COCO

COCO (ECCV'2014) 
@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

For COCO data, please download from COCO download. COCO’2014 Train is needed for HMR training and COCO’2017 Train is needed for HybrIK trainig. Download and extract them under $MMHUMAN3D/data/datasets, and make them look like this:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── coco
            ├── annotations
            |   ├── person_keypoints_train2014.json
            |   ├── person_keypoints_val2014.json
            ├── train2014
            │   ├── COCO_train2014_000000000009.jpg
            │   ├── COCO_train2014_000000000025.jpg
            │   ├── COCO_train2014_000000000030.jpg
            |   └── ...
            └── train_2017
                │── annotations
                │   ├── person_keypoints_train2017.json
                │   └── person_keypoints_val2017.json
                │── train2017
                │   ├── 000000000009.jpg
                │   ├── 000000000025.jpg
                │   ├── 000000000030.jpg
                │   └── ...
                └── val2017
                    ├── 000000000139.jpg
                    ├── 000000000285.jpg
                    ├── 000000000632.jpg
                    └── ...

COCO-WholeBody

COCO-WholeBody (ECCV'2020) 
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

For COCO-WholeBody dataset, images can be downloaded from COCO download, 2017 Train/Val is needed for COCO keypoints training and validation. Download and extract them under $MMHUMAN3D/data/datasets, and make them look like this:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── coco
            ├── annotations
            |   ├── coco_wholebody_train_v1.0.json
            |   └── coco_wholebody_val_v1.0.json
            └── train_2017
                │── train2017
                │   ├── 000000000009.jpg
                │   ├── 000000000025.jpg
                │   ├── 000000000030.jpg
                │   └── ...
                └── val2017
                    ├── 000000000139.jpg
                    ├── 000000000285.jpg
                    ├── 000000000632.jpg
                    └── ...

CrowdPose

CrowdPose (CVPR'2019) 
@article{li2018crowdpose,
  title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
  author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
  journal={arXiv preprint arXiv:1812.00324},
  year={2018}
}

For CrowdPose data, please download from CrowdPose. Download and extract them under $MMHUMAN3D/data/datasets, and make them look like this:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── crowdpose
            ├── crowdpose_train.json
            ├── crowdpose_val.json
            ├── crowdpose_trainval.json
            ├── crowdpose_test.json
            └── images
                ├── 100000.jpg
                ├── 100001.jpg
                ├── 100002.jpg
                └── ...

EFT

EFT (3DV'2021) 
@inproceedings{joo2020eft,
 title={Exemplar Fine-Tuning for 3D Human Pose Fitting Towards In-the-Wild 3D Human Pose Estimation},
 author={Joo, Hanbyul and Neverova, Natalia and Vedaldi, Andrea},
 booktitle={3DV},
 year={2020}
}

For EFT data, please download from EFT. Download and extract them under $MMHUMAN3D/data/datasets, and make them look like this:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
   └── datasets
       └── eft
           ├── coco_2014_train_fit
           |   ├── COCO2014-All-ver01.json
           |   └── COCO2014-Part-ver01.json
           |── LSPet_fit
           |   └── LSPet_ver01.json
           └── MPII_fit
               └── MPII_ver01.json

GTA-Human

GTA-Human (arXiv'2021) 
@article{cai2021playing,
  title={Playing for 3D Human Recovery},
  author={Cai, Zhongang and Zhang, Mingyuan and Ren, Jiawei and Wei, Chen and Ren, Daxuan and Li, Jiatong and Lin, Zhengyu and Zhao, Haiyu and Yi, Shuai and Yang, Lei and others},
  journal={arXiv preprint arXiv:2110.07588},
  year={2021}
}

More details are coming soon!

Human3.6M

Human3.6M (TPAMI'2014) 
@article{h36m_pami,
  author = {Ionescu, Catalin and Papava, Dragos and Olaru, Vlad and Sminchisescu,  Cristian},
  title = {Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  publisher = {IEEE Computer Society},
  volume = {36},
  number = {7},
  pages = {1325-1339},
  month = {jul},
  year = {2014}
}

For Human3.6M, please download from the official website and run the preprocessing script, which will extract pose annotations at downsampled framerate (10 FPS). The processed data should have the following structure:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── h36m
            ├── annot
            ├── S1
            |   ├── images
            |   |    |── S1_Directions_1.54138969
            |   |    |  ├── S1_Directions_1.54138969_00001.jpg
            |   |    |  ├── S1_Directions_1.54138969_00002.jpg
            |   |    |  └── ...
            |   |    └── ...
            |   ├── MyPoseFeatures
            |   |    |── D2Positions
            |   |    └── D3_Positions_Mono
            |   ├── MySegmentsMat
            |   |    └── ground_truth_bs
            |   └── Videos
            |        |── Directions 1.54138969.mp4
            |        |── Directions 1.55011271.mp4
            |        └── ...
            ├── S5
            ├── S6
            ├── S7
            ├── S8
            ├── S9
            ├── S11
            └── metadata.xml

To extract images from Human3.6M original videos, modify the h36m_p1 config in DATASET_CONFIG:

h36m_p1=dict(
    type='H36mConverter',
    modes=['train', 'valid'],
    protocol=1,
    extract_img=True, # set to true to extract images from raw videos
    prefix='h36m'),

Human3.6M Mosh

For data preparation of Human3.6M for HMR and SPIN training, we use the MoShed data provided in HMR for training. However, due to license limitations, we are not allowed to redistribute the data. Even if you do not have access to these parameters, you can still generate the preprocessed h36m npz file without mosh parameters using our converter.

To do so, modify the h36m_p1 config in DATASET_CONFIG:

Config without mosh:

h36m_p1=dict(
    type='H36mConverter',
    modes=['train', 'valid'],
    protocol=1,
    prefix='h36m'),

Config:

h36m_p1=dict(
    type='H36mConverter',
    modes=['train', 'valid'],
    protocol=1,
    mosh_dir='data/datasets/h36m_mosh', # supply the directory to the mosh if available
    prefix='h36m'),

If you have MoShed data available, it should have the following structure:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── h36m_mosh
            ├── annot
            ├── S1
            |   ├── images
            |   |    ├── Directions 1_cam0_aligned.pkl
            |   |    ├── Directions 1_cam1_aligned.pkl
            |   |    └── ...
            ├── S5
            ├── S6
            ├── S7
            ├── S8
            ├── S9
            └── S11

HybrIK

HybrIK (CVPR'2021) 
@inproceedings{li2020hybrikg,
  author = {Li, Jiefeng and Xu, Chao and Chen, Zhicun and Bian, Siyuan and Yang, Lixin and Lu, Cewu},
  title = {HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation},
  booktitle={CVPR 2021},
  pages={3383--3393},
  year={2021},
  organization={IEEE}
}

For HybrIK, please download the parsed json annotation files and place them in the folder structure below:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── hybrik_data
            ├── Sample_5_train_Human36M_smpl_leaf_twist_protocol_2.json
            ├── Sample_20_test_Human36M_smpl_protocol_2.json
            ├── 3DPW_test_new.json
            ├── annotation_mpi_inf_3dhp_train_v2.json
            └── annotation_mpi_inf_3dhp_test.json

To convert the preprocessed json files into npz files used for our pipeline, run the following preprocessing scripts:

LSP

LSP (BMVC'2010) 
@inproceedings{johnson2010clustered,
  title={Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation.},
  author={Johnson, Sam and Everingham, Mark},
  booktitle={bmvc},
  volume={2},
  number={4},
  pages={5},
  year={2010},
  organization={Citeseer}
}

For LSP, please download the high resolution version LSP dataset original. Extract them under $MMHUMAN3D/data/datasets, and make them look like this:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── lsp
            ├── images
            |  ├── im0001.jpg
            |  ├── im0002.jpg
            |  └── ...
            └── joints.mat

LSPET

LSP-Extended (CVPR'2011) 
@inproceedings{johnson2011learning,
  title={Learning effective human pose estimation from inaccurate annotation},
  author={Johnson, Sam and Everingham, Mark},
  booktitle={CVPR 2011},
  pages={1465--1472},
  year={2011},
  organization={IEEE}
}

For LSPET, please download its high resolution form HR-LSPET. Extract them under $MMHUMAN3D/data/datasets, and make them look like this:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── lspet
            ├── im00001.jpg
            ├── im00002.jpg
            ├── im00003.jpg
            ├── ...
            └── joints.mat

MPI-INF-3DHP

MPI_INF_3DHP (3DV'2017) 
@inproceedings{mono-3dhp2017,
 author = {Mehta, Dushyant and Rhodin, Helge and Casas, Dan and Fua, Pascal and Sotnychenko, Oleksandr and Xu, Weipeng and Theobalt, Christian},
 title = {Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision},
 booktitle = {3D Vision (3DV), 2017 Fifth International Conference on},
 url = {http://gvv.mpi-inf.mpg.de/3dhp_dataset},
 year = {2017},
 organization={IEEE},
 doi={10.1109/3dv.2017.00064},
}

For MPI-INF-3DHP, download and extract them under $MMHUMAN3D/data/datasets, and make them look like this:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── mpi_inf_3dhp
            ├── mpi_inf_3dhp_test_set
            │   ├── TS1
            │   ├── TS2
            │   ├── TS3
            │   ├── TS4
            │   ├── TS5
            │   └── TS6
            ├── S1
            │   ├── Seq1
            │   └── Seq2
            ├── S2
            │   ├── Seq1
            │   └── Seq2
            ├── S3
            │   ├── Seq1
            │   └── Seq2
            ├── S4
            │   ├── Seq1
            │   └── Seq2
            ├── S5
            │   ├── Seq1
            │   └── Seq2
            ├── S6
            │   ├── Seq1
            │   └── Seq2
            ├── S7
            │   ├── Seq1
            │   └── Seq2
            └── S8
                ├── Seq1
                └── Seq2

MPII

MPII (CVPR'2014) 
@inproceedings{andriluka14cvpr,
  author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt},
  title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2014},
  month = {June}
}

For MPII data, please download from MPII Human Pose Dataset. Extract them under $MMHUMAN3D/data/datasets, and make them look like this:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── mpii
            |── train.h5
            └── images
                |── 000001163.jpg
                |── 000003072.jpg
                └── ...

PoseTrack18

PoseTrack18 (CVPR'2018) 
@inproceedings{andriluka2018posetrack,
  title={Posetrack: A benchmark for human pose estimation and tracking},
  author={Andriluka, Mykhaylo and Iqbal, Umar and Insafutdinov, Eldar and Pishchulin, Leonid and Milan, Anton and Gall, Juergen and Schiele, Bernt},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5167--5176},
  year={2018}
}

For PoseTrack18 data, please download from PoseTrack18. Extract them under $MMHUMAN3D/data/datasets, and make them look like this:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── posetrack
            ├── images
            │   ├── train
            │   │   ├── 000001_bonn_train
            │   │   │   ├── 000000.jpg
            │   │   │   ├── 000001.jpg
            │   │   │   └── ...
            │   │   └── ...
            │   ├── val
            │   │   ├── 000342_mpii_test
            │   │   │   ├── 000000.jpg
            │   │   │   ├── 000001.jpg
            │   │   │   └── ...
            │   │   └── ...
            │   └── test
            │       ├── 000001_mpiinew_test
            │       │   ├── 000000.jpg
            │       │   ├── 000001.jpg
            │       │   └── ...
            │       └── ...
            └── posetrack_data
                └── annotations
                    ├── train
                    │   ├── 000001_bonn_train.json
                    │   ├── 000002_bonn_train.json
                    │   └── ...
                    ├── val
                    │   ├── 000342_mpii_test.json
                    │   ├── 000522_mpii_test.json
                    │   └── ...
                    └── test
                        ├── 000001_mpiinew_test.json
                        ├── 000002_mpiinew_test.json
                        └── ...

Penn Action

Penn Action (ICCV'2013) 
@inproceedings{zhang2013pennaction,
 title={From Actemes to Action: A Strongly-supervised Representation for Detailed Action Understanding},
 author={Zhang, Weiyu and Zhu, Menglong and Derpanis, Konstantinos},
 booktitle={ICCV},
 year={2013}
}

For Penn Action data, please download from Penn Action. Extract them under $MMHUMAN3D/data/datasets, and make them look like this:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
   └── datasets
       └── penn_action
           ├── frames
           │   ├── 0001
           │   │   ├── 000001.jpg
           │   │   ├── 000002.jpg
           │   │   └── ...
           │   └── ...
           └── labels
               ├── 0001.mat
               ├── 0002.mat
               └── ...

PW3D

PW3D (ECCV'2018) 
@inproceedings{vonMarcard2018,
title = {Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera},
author = {von Marcard, Timo and Henschel, Roberto and Black, Michael and Rosenhahn, Bodo and Pons-Moll, Gerard},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2018},
month = {sep}
}

For PW3D data, please download from PW3D Dataset. Extract them under $MMHUMAN3D/data/datasets, and make them look like this:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── pw3d
            |── imageFiles
            |   |    └── courtyard_arguing_00
            |   |       ├── image_00000.jpg
            |   |       ├── image_00001.jpg
            |   |       └── ...
            └── sequenceFiles
                ├── train
                │   ├── downtown_arguing_00.pkl
                │   └── ...
                ├── val
                │   ├── courtyard_arguing_00.pkl
                │   └── ...
                └── test
                    ├── courtyard_basketball_00.pkl
                    └── ...

SPIN

SPIN (ICCV'2019) 
@inproceedings{kolotouros2019spin,
  author = {Kolotouros, Nikos and Pavlakos, Georgios and Black, Michael J and Daniilidis, Kostas},
  title = {Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop},
  booktitle={ICCV},
  year={2019}
}

For SPIN, please download the preprocessed npz files and place them in the folder structure below:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    └── datasets
        └── spin_data
            ├── coco_2014_train.npz
            ├── hr-lspet_train.npz
            ├── lsp_dataset_original_train.npz
            ├── mpi_inf_3dhp_train.npz
            └── mpii_train.npz

SURREAL

SURREAL (CVPR'2017) 
@inproceedings{varol17_surreal,
 title     = {Learning from Synthetic Humans},
 author    = {Varol, G{\"u}l and Romero, Javier and Martin, Xavier and Mahmood, Naureen and Black, Michael J. and Laptev, Ivan and Schmid, Cordelia},
 booktitle = {CVPR},
 year      = {2017}
}

For SURREAL, please download the [dataset] (https://www.di.ens.fr/willow/research/surreal/data/) and place them in the folder structure below:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
   └── datasets
       └── surreal
          ├── train
          │   ├── run0
          |   |    ├── 03_01
          |   |    │   ├── 03_01_c0001_depth.mat
          |   |    │   ├── 03_01_c0001_info.mat
          |   |    │   ├── 03_01_c0001_segm.mat
          |   |    │   ├── 03_01_c0001.mp4
          |   |    │   └── ...
          |   |    └── ...
          │   ├── run1
          │   └── run2
          ├── val
          │   ├── run0
          │   ├── run1
          │   └── run2
          └── test
              ├── run0
              ├── run1
              └── run2

VIBE

VIBE (CVPR'2020) 
@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}
}

For VIBE, please download the preprocessed mpi_inf_3dhp and pw3d npz files from SPIN and pretrained frame feature extractor spin.pth. Place them in the folder structure below:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    ├── checkpoints
    |   └── spin.pth
    └── datasets
        └── vibe_data
            ├── mpi_inf_3dhp_train.npz
            └── pw3d_test.npz
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