PyTorch implementation of hand mesh reconstruction described in CMR and MobRecon.

Overview

Hand Mesh Reconstruction

Introduction

This repo is the PyTorch implementation of hand mesh reconstruction described in CMR and MobRecon.

Update

  • 2021-12.7, Add MobRecon demo.
  • 2021-6-10, Add Human3.6M dataset.
  • 2021-5-20, Add CMR-G model.

Features

  • SpiralNet++
  • Sub-pose aggregation
  • Adaptive 2D-1D registration for mesh-image alignment
  • DenseStack for 2D encoding
  • Feature lifting with MapReg and PVL
  • DSConv as an efficient mesh operator
  • MobRecon training with consistency learning and complement data

Install

  • Environment

    conda create -n handmesh python=3.6
    conda activate handmesh
    
  • Please follow official suggestions to install pytorch and torchvision. We use pytorch=1.7.1, torchvision=0.8.2

  • Requirements

    pip install -r requirements.txt
    

    If you have difficulty in installing torch_sparse etc., please use whl file from here.

  • MPI-IS Mesh: We suggest to install this library from the source

  • Download the files you need from Google drive.

Run a demo

  • Prepare pre-trained models as

    out/Human36M/cmr_g/checkpoints/cmr_g_res18_human36m.pt
    out/FreiHAND/cmr_g/checkpoints/cmr_g_res18_moredata.pt
    out/FreiHAND/cmr_sg/checkpoints/cmr_sg_res18_freihand.pt
    out/FreiHAND/cmr_pg/checkpoints/cmr_pg_res18_freihand.pt  
    out/FreiHAND/mobrecon/checkpoints/mobrecon_densestack_dsconv.pt  
    
  • Run

    ./scripts/demo_cmr.sh
    ./scripts/demo_mobrecon.sh
    

    The prediction results will be saved in output directory, e.g., out/FreiHAND/mobrecon/demo.

  • Explaination of the output

    • In an JPEG file (e.g., 000_plot.jpg), we show silhouette, 2D pose, projection of mesh, camera-space mesh and pose
    • As for camera-space information, we use a red rectangle to indicate the camera position, or the image plane. The unit is meter.
    • If you run the demo, you can also obtain a PLY file (e.g., 000_mesh.ply).
      • This file is a 3D model of the hand.
      • You can open it with corresponding software (e.g., Preview in Mac).
      • Here, you can get more 3D details through rotation and zoom in.

Dataset

FreiHAND

  • Please download FreiHAND dataset from this link, and create a soft link in data, i.e., data/FreiHAND.
  • Download mesh GT file freihand_train_mesh.zip, and unzip it under data/FreiHAND/training

Human3.6M

  • The official data is now not avaliable. Please follow I2L repo to download it.
  • Download silhouette GT file h36m_mask.zip, and unzip it under data/Human36M.

Data dir

${ROOT}  
|-- data  
|   |-- FreiHAND
|   |   |-- training
|   |   |   |-- rgb
|   |   |   |-- mask
|   |   |   |-- mesh
|   |   |-- evaluation
|   |   |   |-- rgb
|   |   |-- evaluation_K.json
|   |   |-- evaluation_scals.json
|   |   |-- training_K.json
|   |   |-- training_mano.json
|   |   |-- training_xyz.json
|   |-- Human3.6M
|   |   |-- images
|   |   |-- mask
|   |   |-- annotations

Evaluation

FreiHAND

./scripts/eval_cmr_freihand.sh
./scripts/eval_mobrecon_freihand.sh
  • JSON file will be saved as out/FreiHAND/cmr_sg/cmr_sg.josn. You can submmit this file to the official server for evaluation.

Human3.6M

./scripts/eval_cmr_human36m.sh

Performance on PA-MPJPE (mm)

We re-produce the following results after code re-organization.

Model / Dataset FreiHAND Human3.6M (w/o COCO)
CMR-G-ResNet18 7.6 -
CMR-SG-ResNet18 7.5 -
CMR-PG-ResNet18 7.5 50.0
MobRecon-DenseStack 6.9 -

Training

./scripts/train_cmr_freihand.sh
./scripts/train_cmr_human36m.sh

Reference

@inproceedings{bib:CMR,
  title={Camera-Space Hand Mesh Recovery via Semantic Aggregationand Adaptive 2D-1D Registration},
  author={Chen, Xingyu and Liu, Yufeng and Ma, Chongyang and Chang, Jianlong and Wang, Huayan and Chen, Tian and Guo, Xiaoyan and Wan, Pengfei and Zheng, Wen},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021}
}
@article{bib:MobRecon,
  title={MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Image},
  author={Chen, Xingyu and Liu, Yufeng and Dong Yajiao and Zhang, Xiong and Ma, Chongyang and Xiong, Yanmin and Zhang, Yuan and Guo, Xiaoyan},
  journal={arXiv:2112.02753},
  year={2021}
}
}

Acknowledgement

Our implementation of SpiralConv is based on spiralnet_plus.

Owner
Xingyu Chen
Xingyu Chen
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