Live Stream Temporally Embedded 3D Human Body Pose and Shape Estimation

Contact:

Zhouping Wang

Sarah Ostadabbas

Introduction

This repository is the official PyTorch implementation of [Live Stream Temporally Embedded 3D Human Body Pose and Shape Estimation]. The base codes are largely borrowed from TCMR.

Installation

TePose is developed using python 3.7 on Ubuntu 18.04. You may need sudo privilege for the installation.

source scripts/install_pip.sh

Demo

• Download the pretrained SMPL layers from here (male&female), here (neutral) and SMPL regressor. Put SMPL layers (pkl files) under ${ROOT}/data/base_data/. These opeartions can be realized with follwing code

source scripts/get_base_data.sh

• Download pre-trained TePose weights.

• Run demo with options (e.g. render on plain background). See more option details in bottom lines of demo.py.

• A video overlayed with rendered meshes will be saved in ${ROOT}/output/demo_output/.

python demo.py --vid_file merged_courtyard_basketball_01.mp4 --gpu 0 

Running TePose

Pre-processed PoseTrack, 3DPW, MPI-INF-3DHP and Human3.6M are uploaded by TCMR authors from here. Pre-processed InstaVariety is uploaded by VIBE authors here. AMASS can be generated following the instruction provided by VIBE authors here. Download pseudo SMPL labels from here. You could also generate it by yourself with following code

source scripts/prepare_pseudo_thetas.sh

The data directory structure should follow the below hierarchy.

${ROOT}  
|-- data  
|   |-- base_data  
|   |   ├── J_regressor_extra.npy
|   |   :
|   |   └── spin_model_checkpoint.pth.tar
|   |-- preprocessed_data  
|   |   ├── 3dpw_test_all_db.pt
|   |   ├── 3dpw_test_all_pseudotheta.pt
|   |   :
|   |   ├── posetrack_train_db.pt
|   |   └── posetrack_train_pseudotheta.pt
|   |-- pretrained_models
|   |   ├── tepose_wopw_3dpw_test.pth.tar
|   |   :
|   |   └── tepose_wpw_mpii3d_h36m_test.pth.tar
|-- demo.py
:
:
|-- merged_courtyard_basketball_01.mp4

Evaluation

• Run the evaluation code with a corresponding config file to get performance in different settings. You could also check the visual performance by adding "--render" at the end of the command.

# dataset: 3dpw, mpii3d, h36m 
python evaluate.py --dataset 3dpw --cfg ./configs/repr_wpw_3dpw_model.yaml --gpu 0 
python evaluate.py --dataset 3dpw --cfg ./configs/repr_wopw_3dpw_model.yaml --gpu 0 
python evaluate.py --dataset h36m --cfg ./configs/repr_wpw_h36m_mpii3d_model.yaml --gpu 0 
python evaluate.py --dataset h36m --cfg ./configs/repr_wopw_h36m_model.yaml --gpu 0 
python evaluate.py --dataset mpii3d --cfg ./configs/repr_wpw_h36m_mpii3d_model.yaml --gpu 0 
python evaluate.py --dataset mpii3d --cfg ./configs/repr_wopw_mpii3d_model.yaml --gpu 0 

• You may test options such as average filtering and rendering. See the bottom lines of ${ROOT}/lib/core/config.py.

Results

We compare proposed TePose with state-of-the-art methods in the following tables.

Reproduction (Training)

• TePose models with different settings can be trained with different configuration file. For example, training model without 3DPW as train set for evaluating on 3DPW test set (results of 4 columns on the right of first table) can be realized by the first command as follows

# training outputs are saved in `experiments` directory
# mkdir experiments
python train.py --cfg ./configs/repr_wpw_3dpw_model.yaml --gpu 0 
python train.py --cfg ./configs/repr_wpw_h36m_mpii3d_model.yaml --gpu 0 
python train.py --cfg ./configs/repr_wopw_3dpw_model.yaml --gpu 0 
python train.py --cfg ./configs/repr_wopw_h36m_model.yaml --gpu 0
python train.py --cfg ./configs/repr_wopw_mpii3d_model.yaml --gpu 0  

• After the training, the checkpoints are saved in ${ROOT}/experiments/{date_of_training}/. Change the config file's TRAIN.PRETRAINED with the checkpoint path (either checkpoint.pth.tar or model_best.pth.tar) and follow the evaluation command.

Reference

@inproceedings{wang2022tepose,
  title={Live Stream Temporally Embedded 3D Human Body Pose and Shape Estimation},
  author={Wang, Zhouping and Ostadabbas, Sarah},
  booktitle={arXiv preprint: https://arxiv.org/pdf/2207.12537.pdf},
  month     = {July},
  year      = {2022}
}

License

• This code is for non-commercial purpose only.
• For further inquiry please contact: Augmented Cognition Lab at Northeastern University: http://www.northeastern.edu/ostadabbas/