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<div align="center"> <h1 align="center">Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Video (ICCV 2023)</h1> </div> <div align="left">

<a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a> <a href="https://kasvii.github.io/PMCE/"><img alt="Project" src="https://img.shields.io/badge/-Project%20Page-lightgrey?logo=Google%20Chrome&color=informational&logoColor=white"></a> <a href="https://youtu.be/slSPQ9hNLjM"> arXiv <a href="https://github.com/kasvii/PMCE/blob/main/LICENSE">license</a>

</div>

This is the offical Pytorch implementation of the paper:

<h3 align="center">Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Video (ICCV 2023)</h3> <h4 align="center" style="text-decoration: none;"> <a href="https://kasvii.github.io/", target="_blank"><b>Yingxuan You</b></a> , <a href="https://scholar.google.com/citations?hl=zh-CN&user=4CQKG8oAAAAJ", target="_blank"><b>Hong Liu</b></a> , <a href="https://scholar.google.com/citations?user=PjBAErYAAAAJ&hl=zh-CN&oi=sra", target="_blank"><b>Ti Wang</b></a> , <a href="https://vegetebird.github.io/", target="_blank"><b>Wenhao Li</b></a> , <a href="https://scholar.google.com/citations?user=gU9chAwAAAAJ&hl=zh-CN&oi=sra", target="_blank"><b>Runwei Ding</b></a>, <a href="https://xialipku.github.io/", target="_blank"><b>Xia Li</b></a> </h4> <h4 align="center"> <a href="https://kasvii.github.io/PMCE/", target="_blank">project page</a> / <a href="https://arxiv.org/pdf/2308.10305.pdf", target="_blank">arXiv</a> / <a href="https://openaccess.thecvf.com/content/ICCV2023/papers/You_Co-Evolution_of_Pose_and_Mesh_for_3D_Human_Body_Estimation_ICCV_2023_paper.pdf", target="_blank">paper</a> / <a href="https://openaccess.thecvf.com/content/ICCV2023/supplemental/You_Co-Evolution_of_Pose_ICCV_2023_supplemental.pdf", target="_blank">supplementary</a> </h4> <p align="center"> <img src="assets/framework.png" /> </p> <p align="center"> <img src="assets/demo1.gif" height="110" /> <img src="assets/demo2.gif" height="110" /> <img src="assets/demo3.gif" height="110" /> <img src="assets/demo4.gif" height="110" /> </p>

Preparation

  1. Install dependencies. This project is developed on Ubuntu 18.04 with NVIDIA 3090 GPUs. We recommend you to use an Anaconda virtual environment.
# Create a conda environment.
conda create -n pmce python=3.8
conda activate pmce

# Install PyTorch >= 1.2 according to your GPU driver.
conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge

# Pull the code
git clone https://github.com/kasvii/PMCE.git
cd PMCE

# Install other dependencies.
sh requirements.sh
  1. Prepare SMPL layer.
  1. Download base data

Quick Demo

  1. Install ViTPose. PMCE uses the off-the-shift 2D pose detectors to detect persons from images. Here we take and install ViTPose.
git clone https://github.com/open-mmlab/mmcv.git
cd mmcv
git checkout v1.3.9
MMCV_WITH_OPS=1 pip install -e .
cd ..
git clone https://github.com/ViTAE-Transformer/ViTPose.git
cd ViTPose
pip install -v -e .
  1. Download the pre-trained ViTPose model vitpose-h-multi-coco.pth from OneDrive. Put it below ./pose_detector folder.
  2. Download the pre-trained PMCE model mesh_vis.pth.tar from OneDrive. Put it below ./experiment/pretrained folder.
  3. Prepare the input video *.mp4 and put it below ./demo folder.
  4. Run. The output is at ./output folder.
# Change 'sample_video' to your video name.
python ./main/run_demo.py --vid_file demo/sample_video.mp4 --gpu 0

Implementation

Data Preparation

The ./data directory structure should follow the below hierarchy. Download all the processed annotation files from OneDrive

${Project}  
|-- data  
|   |-- base_data
|   |   |-- J_regressor_extra.npy
|   |   |-- mesh_downsampling.npz
|   |   |-- smpl_mean_params.npz
|   |   |-- smpl_mean_vertices.npy
|   |   |-- SMPL_NEUTRAL.pkl
|   |   |-- spin_model_checkpoint.pth.tar
|   |-- COCO  
|   |   |-- coco_data  
|   |   |-- __init__.py
|   |   |-- dataset.py
|   |   |-- J_regressor_coco.npy
|   |-- Human36M  
|   |   |-- h36m_data  
|   |   |-- __init__.py
|   |   |-- dataset.py 
|   |   |-- J_regressor_h36m_correct.npy
|   |   |-- noise_stats.py
|   |-- MPII  
|   |   |-- mpii_data  
|   |   |-- __init__.py
|   |   |-- dataset.py
|   |-- MPII3D
|   |   |-- mpii3d_data  
|   |   |-- __init__.py
|   |   |-- dataset.py
|   |-- PW3D 
|   |   |-- pw3d_data
|   |   |-- __init__.py
|   |   |-- dataset.py
|   |-- multiple_datasets.py

Test

To test on a pre-trained pose estimation model (Stage 1).

# Human3.6M
bash test_pose_h36m.sh

# 3DPW
bash test_pose_3dpw.sh

To test on a pre-trained mesh model (Stage 2).

# Human3.6M
bash test_mesh_h36m.sh

# 3DPW
bash test_mesh_3dpw.sh

# MPII3D
bash test_mesh_mpii3d.sh

Change the weight_path in the corresponding ./config/test_*.yml to your model path.

Train

Stage 1 (optional): Train the 3D pose estimation stream or you can directly use our pre-traind pose model ./experiment/pretrained/pose_*.pth.tar for Stage 2.

# Human3.6M
bash train_pose_h36m.sh

# 3DPW
bash train_pose_3dpw.sh

Stage 2: To train the all network for final mesh. Configs of the experiments can be found and edited in ./config folder. Change posenet_path in ./config/train_mesh_*.yml to the path of the pre-trained pose model.

# Human3.6M
bash train_mesh_h36m.sh

# 3DPW & MPII3D
bash train_mesh_3dpw.sh

Citation

Cite as below if you find this repository is helpful to your project:

@inproceedings{you2023co,
  title     = {Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Video},
  author    = {You, Yingxuan and Liu, Hong and Wang, Ti and Li, Wenhao and Ding, Runwei and Li, Xia},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages     = {14963--14973},
  year      = {2023}
}

Acknowledgement

This repo is extended from the excellent work Pose2Mesh, TCMR. We thank the authors for releasing the codes.