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Getting Started

  1. Create folders that store pretrained models, datasets, and predictions.

    export REPO_DIR=$PWD
mkdir -p $REPO_DIR/models  # pre-trained models
mkdir -p $REPO_DIR/datasets  # datasets
mkdir -p $REPO_DIR/predictions  # prediction outputs

  2. Download pretrained models.

    Our pre-trained models can be downloaded with the following command.

    cd $REPO_DIR
bash scripts/download_models.sh

    The scripts will download three models that are trained for mesh reconstruction on Human3.6M, 3DPW, and FreiHAND, respectively. For your convenience, this script will also download HRNet pre-trained weights, which will be used in training.

    The resulting data structure should follow the hierarchy as below.

    ${REPO_DIR}  
|-- models  
|   |-- metro_release
|   |   |-- metro_h36m_state_dict.bin
|   |   |-- metro_3dpw_state_dict.bin
|   |   |-- metro_hand_state_dict.bin
|   |-- hrnet
|   |   |-- hrnetv2_w40_imagenet_pretrained.pth
|   |   |-- hrnetv2_w64_imagenet_pretrained.pth
|   |   |-- cls_hrnet_w40_sgd_lr5e-2_wd1e-4_bs32_x100.yaml
|   |   |-- cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml
|-- metro 
|-- datasets 
|-- predictions 
|-- README.md 
|-- ... 
|-- ...

  3. Download SMPL and MANO models

    To run our code smoothly, please visit the following websites to download SMPL and MANO models.

    • Download basicModel_neutral_lbs_10_207_0_v1.0.0.pkl from SMPLify, and place it at ${REPO_DIR}/metro/modeling/data.
    • Download MANO_RIGHT.pkl from MANO, and place it at ${REPO_DIR}/metro/modeling/data.

    Please put the downloaded files under the ${REPO_DIR}/metro/modeling/data directory. The data structure should follow the hierarchy below.

    ${REPO_DIR}  
|-- metro  
|   |-- modeling
|   |   |-- data
|   |   |   |-- basicModel_neutral_lbs_10_207_0_v1.0.0.pkl
|   |   |   |-- MANO_RIGHT.pkl
|-- models
|-- datasets
|-- predictions
|-- README.md 
|-- ... 
|-- ...

    Please check /metro/modeling/data/README.md for further details.

  4. Download prediction files that were evaluated on FreiHAND Leaderboard.

    The prediction files can be downloaded with the following command.

    cd $REPO_DIR
bash scripts/download_preds.sh

    You could submit the prediction files to FreiHAND Leaderboard and reproduce our results.

  5. Download datasets and pseudo labels for training.

    We recommend to download large files with AzCopy for faster speed. AzCopy executable tools can be downloaded here. Decompress the azcopy tar file and put the executable in any path.

    To download the annotation files, please use the following command.

    cd $REPO_DIR
path/to/azcopy copy 'https://datarelease.blob.core.windows.net/metro/datasets/filename.tar' /path/to/your/folder/filename.tar
tar xvf filename.tar

    filename.tar could be Tax-H36m-coco40k-Muco-UP-Mpii.tar, human3.6m.tar, coco_smpl.tar, muco.tar, up3d.tar, mpii.tar, 3dpw.tar, freihand.tar. Total file size is about 200 GB.

    The datasets and pseudo ground truth labels are provided by Pose2Mesh. We only reorganize the data format to better fit our training pipeline. We suggest to download the orignal image files from the offical dataset websites.

    The datasets directory structure should follow the below hierarchy.

    ${ROOT}  
|-- datasets  
|   |-- Tax-H36m-coco40k-Muco-UP-Mpii  
|   |   |-- train.yaml 
|   |   |-- train.linelist.tsv  
|   |   |-- train.linelist.lineidx
|   |-- human3.6m  
|   |   |-- train.img.tsv 
|   |   |-- train.hw.tsv 
|   |   |-- train.linelist.tsv    
|   |   |-- smpl/train.label.smpl.p1.tsv
|   |   |-- smpl/train.linelist.smpl.p1.tsv
|   |   |-- valid.protocol2.yaml
|   |   |-- valid_protocol2/valid.img.tsv 
|   |   |-- valid_protocol2/valid.hw.tsv  
|   |   |-- valid_protocol2/valid.label.tsv
|   |   |-- valid_protocol2/valid.linelist.tsv
|   |-- coco_smpl  
|   |   |-- train.img.tsv  
|   |   |-- train.hw.tsv   
|   |   |-- smpl/train.label.tsv
|   |   |-- smpl/train.linelist.tsv
|   |-- muco  
|   |   |-- train.img.tsv  
|   |   |-- train.hw.tsv   
|   |   |-- train.label.tsv
|   |   |-- train.linelist.tsv
|   |-- up3d  
|   |   |-- trainval.img.tsv  
|   |   |-- trainval.hw.tsv   
|   |   |-- trainval.label.tsv
|   |   |-- trainval.linelist.tsv
|   |-- mpii  
|   |   |-- train.img.tsv  
|   |   |-- train.hw.tsv   
|   |   |-- train.label.tsv
|   |   |-- train.linelist.tsv
|   |-- 3dpw 
|   |   |-- train.img.tsv  
|   |   |-- train.hw.tsv   
|   |   |-- train.label.tsv
|   |   |-- train.linelist.tsv
|   |   |-- test_has_gender.yaml
|   |   |-- has_gender/test.img.tsv 
|   |   |-- has_gender/test.hw.tsv  
|   |   |-- has_gender/test.label.tsv
|   |   |-- has_gender/test.linelist.tsv
|   |-- freihand
|   |   |-- train.yaml
|   |   |-- train.img.tsv  
|   |   |-- train.hw.tsv   
|   |   |-- train.label.tsv
|   |   |-- train.linelist.tsv
|   |   |-- test.yaml
|   |   |-- test.img.tsv  
|   |   |-- test.hw.tsv   
|   |   |-- test.label.tsv
|   |   |-- test.linelist.tsv
|-- metro
|-- models 
|-- predictions
|-- README.md 
|-- ... 
|-- ...