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JGR-P2O

This is the Tensorflow implementation of our ECCV2020 paper "JGR-P2O: Joint Graph Reasoning based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image"

The key ideas of JGR-P2O are two-fold: a) explicitly modeling the dependencies among joints and the relations between the pixels and the joints with the joint graph reasoning module for better local feature representation learning; b) unifying the dense pixel-wise offset predictions and direct joint regression for end-to-end training.

<div align=center> <img src="https://user-images.githubusercontent.com/22862577/87033371-b29f2800-c218-11ea-83be-0a34551c3288.png"><br> Figure 1: Overview of JGR-P2O. </div>

Run

  1. Requirements:

    • python3, tensorflow 1.12-14

  2. datasets:

    • Download the NYU, ICVL, and MSRA datasets.
    • Download the hand centers files from V2V-PoseNet for data preprocessing.
    • Thanks CrossInfoNet for providing a good reference of data preprocessing.

  3. training and testing:

    • Here we provide an example for NYU training and testing, running on ICVL and MSRA is the same way.
    • training: python main_nyu.py --phase train --data_root /home/data/3D_hand_pose_estimation/NYU/
    • testing: python main_nyu.py --phase test --data_root /home/data/3D_hand_pose_estimation/NYU/

  4. Models:

    • The pretrained models on NYU and ICVL are included in the "checkpoint" directory of this repository.