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[ECCV 2022] S2Contact: Graph-based Network for 3D Hand-Object Contact Estimation with Semi-Supervised Learning

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S2Contact: Graph-based Network for 3D Hand-Object Contact Estimation with Semi-Supervised Learning

This repo contains details for our paper: "S2Contact: Graph-based Network for 3D Hand-Object Contact Estimation with Semi-Supervised Learning" (ECCV 2022)

report

[Project Page]

Teaser

Installation

This document contains detailed instructions for installing the necessary dependencied for S2Contact.

  • Create and activate a conda environment

    conda create -n s2contact python=3.8
    conda activate s2contact
  • Install PyTorch and PyTorch3D

    conda install -c pytorch pytorch=1.7.1 torchvision cudatoolkit=10.2
    conda install -c fvcore -c iopath -c conda-forge fvcore iopath
    conda install pytorch3d -c pytorch3d
  • Install PyTorch-Geometric Please following this installation instructions

  • Install Other dependencies

    pip install git+https://github.com/hassony2/manopth.git open3d tensorboardX pyquaternion trimesh transforms3d chumpy opencv-python
  • Download MANO Model Download the MANO model files (mano_v1_2.zip) from MANO website.

    mano/webuser/lbs.py
    mano/models/MANO_RIGHT.pkl

Quick Start

  • Quick Demo
    python network/run_contactopt.py --split=demo --model=dgcnn
    python network/run_eval.py --split=demo --model=dgcnn
    python network/run_eval.py --split=demo --model=dgcnn --vis

TODO

  • To release pseudo labeled dataset.
  • Upload paper to arXiv.

Acknowledgement

This repo is built upon ContactOpt. We would like to thank their authors for providing great frameworks and toolkits.

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[ECCV 2022] S2Contact: Graph-based Network for 3D Hand-Object Contact Estimation with Semi-Supervised Learning

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