HichemSaoudi / ST-SGCN

ST-SGCN

This repository contains the source code for our paper: Spatio-Temporal Sparse Graph Convolution Network for Hand Gesture Recognition

Our approach

The main steps include:

1. Decoupled learning of directed and sparse spatial and temporal interactions, using attention mechanism and asymmetric convolutions.

2. Acquiring cross-spatial-temporal graph representations, by capturing both spatial-temporal and temporal-spatial interactions.

Updates

• ...

Installation

Create and activate conda environment:

conda create -n stsgcn_env python=3.10
conda activate stsgcn_env

Install all dependencies:

pip install -r requirements.txt

Dataset

By default, the 3D hand joints training dataset is structured as follows for each sequence (see example in ./datasets/):

## frames 1
x1, y1, z1
x2, y2, z2
...
...
x21, y21, z21

## frame 2
x1, y1, z1
x2, y2, z2
...
...
x21, y21, z21

## frame t
...

## frame T
x1, y1, z1
x2, y2, z2
...
...
x21, y21, z21

Training

1. Download the SHREC’17 Track Dataset , the Briareo Dataset , the SHREC'21 Dataset and the Extracted landmarks IPN Dataset
2. Set the path to you dataset in train.py (line 107)
3. Run the following command to train the model

python train.py --params

1. Download pre-trained models : Briareo , SHREC17 , SHREC21 and IPN

Citation

If you find this repo useful, please consider citing our paper

ref

Notes

This code borrows from SGCN.