This repository contains the code for the following paper:

3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
Hongwen Zhang*, Yating Tian*, Xinchi Zhou, Wanli Ouyang, Yebin Liu, Limin Wang, Zhenan Sun


  • Python 3.6.10


necessary files

mesh_downsampling.npz & DensePose UV data

  • Run the following script to fetch mesh_downsampling.npz & DensePose UV data from other repositories.

SMPL model files

Fetch preprocessed data from SPIN.

Download the pre-trained model and put it into the ./data/pretrained_model directory.

After collecting the above necessary files, the directory structure of ./data is expected as follows.

├── dataset_extras
│   └── .npz files
├── J_regressor_extra.npy
├── J_regressor_h36m.npy
├── mesh_downsampling.npz
├── pretrained_model
│   └──
├── smpl
│   ├── SMPL_FEMALE.pkl
│   ├── SMPL_MALE.pkl
│   └── SMPL_NEUTRAL.pkl
├── smpl_mean_params.npz
├── static_fits
│   └── .npy files
└── UV_data
    ├── UV_Processed.mat
    └── UV_symmetry_transforms.mat


[UPDATE] You can first give it a try on Google Colab using the notebook we have prepared, which is no need to prepare the environment yourself: Open In Colab

Run the demo code.

python3 --checkpoint=data/pretrained_model/ --vid_file ./flashmob.mp4

Frame by frame reconstruction. Video clipped from here.


Human3.6M / 3DPW

Run the evaluation code. Using --dataset to specify the evaluation dataset.

# Example usage:

# Human3.6M Protocol 2
python3 --checkpoint=data/pretrained_model/ --dataset=h36m-p2 --log_freq=20

# 3DPW
python3 --checkpoint=data/pretrained_model/ --dataset=3dpw --log_freq=20

COCO Keypoint Localization

  1. Download the preprocessed data coco_2014_val.npz. Put it into the ./data/dataset_extras directory.

  2. Run the COCO evaluation code.

python3 --checkpoint=data/pretrained_model/


To perform training, we need to collect preprocessed files of training datasets at first.

The preprocessed labels have the same format as SPIN and can be retrieved from here. Please refer to SPIN for more details about data preprocessing.

PyMAF is trained on Human3.6M at the first stage and then trained on the mixture of both 2D and 3D datasets at the second stage. Example usage:

# training on Human3.6M
CUDA_VISIBLE_DEVICES=0 python3 --regressor pymaf_net --single_dataset --misc TRAIN.BATCH_SIZE 64
# training on mixed datasets
CUDA_VISIBLE_DEVICES=0 python3 --regressor pymaf_net --pretrained_checkpoint path/to/ --misc TRAIN.BATCH_SIZE 64

Running the above commands will use Human3.6M or mixed datasets for training, respectively. We can monitor the training process by setting up a TensorBoard at the directory ./logs.


If this work is helpful in your research, please cite the following paper.

  title={3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop},
  author={Zhang, Hongwen and Tian, Yating and Zhou, Xinchi and Ouyang, Wanli and Liu, Yebin and Wang, Limin and Sun, Zhenan},
  journal={arXiv preprint arXiv:2103.16507},