Note: We follow the guideline provided by MeshTransformer/docs/DOWNLOAD.md

Download

Getting Started

1. Create folders that store pre-trained models and datasets.

   export REPO_DIR=$PWD
   mkdir -p $REPO_DIR/models  # pre-trained models
   mkdir -p $REPO_DIR/models/fastmetro_checkpoint  # model checkpoints
   mkdir -p $REPO_DIR/datasets  # datasets
   

2. Download HRNet-W64 pre-trained on ImageNet.

   HRNet-W64 pre-trained models can be downloaded with the following command.

   cd $REPO_DIR
   bash scripts/download_hrnet.sh
   

   The resulting data structure should follow the hierarchy as below.

   ${REPO_DIR}  
   |-- models  
   |   |-- fastmetro_checkpoint
   |   |-- hrnet
   |   |   |-- hrnetv2_w64_imagenet_pretrained.pth
   |   |   |-- cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml
   |-- src 
   |-- datasets 
   |-- README.md 
   |-- ... 
   |-- ... 
   

3. Download model checkpoints. Please put the downloaded files under the ${REPO_DIR}/models/fastmetro_checkpoint directory.

   (Non-Parametric) FastMETRO

   Model	Dataset	PA-MPJPE	Link
   FastMETRO-S-R50	Human3.6M	38.8	Download
   FastMETRO-S-R50	3DPW	49.1	Download
   FastMETRO-L-H64	Human3.6M	33.6	Download
   FastMETRO-L-H64	3DPW	44.6	Download
   FastMETRO-L-H64	FreiHAND	6.5	Download

   (Parametric) FastMETRO with an optional SMPL parameter regressor

   Model	Dataset	PA-MPJPE	Link
   FastMETRO-L-H64	Human3.6M	36.1	Download
   FastMETRO-L-H64	3DPW	51.0	Download

   • Model checkpoints were obtained in Conda Environment (CUDA 11.1)
   • To use SMPL parameter regressor, you need to set --use_smpl_param_regressor as True

   The resulting data structure would follow the hierarchy as below.

   ${REPO_DIR}  
   |-- models  
   |   |-- fastmetro_checkpoint
   |   |   |-- FastMETRO-L-H64_h36m_state_dict.bin
   |   |   |-- FastMETRO-L-H64_3dpw_state_dict.bin
   |   |   |-- FastMETRO-L-H64_freihand_state_dict.bin
   |   |   |-- FastMETRO-L-H64_smpl_h36m_state_dict.bin
   |   |   |-- FastMETRO-L-H64_smpl_3dpw_state_dict.bin
   |   |   |-- ...
   |   |   |-- ...
   |   |-- hrnet
   |   |   |-- hrnetv2_w64_imagenet_pretrained.pth
   |   |   |-- cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml
   |-- src 
   |-- datasets 
   |-- README.md 
   |-- ... 
   |-- ... 
   

4. 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}/src/modeling/data.
   • Download MANO_RIGHT.pkl from MANO, and place it at ${REPO_DIR}/src/modeling/data.

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

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

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

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}  
   |-- models 
   |-- src
   |-- 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
   |-- README.md 
   |-- ... 
   |-- ...