# Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention

arXiv: https://arxiv.org/abs/1907.08871 · HTML: https://mlsanity.com/paper/1907.08871/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2019.

## Built on by (most cited first, 22 of 22)

- [Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning](https://mlsanity.com/paper/2207.09644/) (2022)
- [No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces](https://mlsanity.com/paper/2203.11113/) (2022)
- [Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos](https://mlsanity.com/paper/2109.07353/) (2021)
- [The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition](https://mlsanity.com/paper/2105.13061/) (2021)
- [A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities](https://mlsanity.com/paper/2408.05436/) (2024)
- [HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition](https://mlsanity.com/paper/2106.13391/) (2021)
- [A Deep Learning-based Multimodal Depth-Aware Dynamic Hand Gesture Recognition System](https://mlsanity.com/paper/2107.02543/) (2021)
- [A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets](https://mlsanity.com/paper/2204.03328/) (2022)
- [Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning](https://mlsanity.com/paper/2305.12959/) (2023)
- [OO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation](https://mlsanity.com/paper/2304.05956/) (2023)
- [Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN](https://mlsanity.com/paper/2406.15003/) (2024)
- [Data-Free Class-Incremental Gesture Recognition with Prototype-Guided Pseudo Feature Replay](https://mlsanity.com/paper/2505.20049/) (2025)
- [Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene](https://mlsanity.com/paper/2109.02917/) (2021)
- [Boosting Gesture Recognition with an Automatic Gesture Annotation Framework](https://mlsanity.com/paper/2401.11150/) (2024)
- [Bengali Sign Language Recognition through Hand Pose Estimation using Multi-Branch Spatial-Temporal Attention Model](https://mlsanity.com/paper/2408.14111/) (2024)
- [Online hand gesture recognition using Continual Graph Transformers](https://mlsanity.com/paper/2502.14939/) (2025)
- [Study and Survey on Gesture Recognition Systems](https://mlsanity.com/paper/2312.00392/) (2023)
- [One-Shot Multi-Rate Pruning of Graph Convolutional Networks](https://mlsanity.com/paper/2312.17615/) (2023)
- [Continual Gesture Learning without Data via Synthetic Feature Sampling](https://mlsanity.com/paper/2408.12629/) (2024)
- [POET: Prompt Offset Tuning for Continual Human Action Adaptation](https://mlsanity.com/paper/2504.18059/) (2025)
- [OMG-Bench: A New Challenging Benchmark for Skeleton-based Online Micro Hand Gesture Recognition](https://mlsanity.com/paper/2512.16727/) (2025)
- [Real-Time Hand Gesture Recognition for OpenXR Using Transformer-Based Machine Learning](https://mlsanity.com/paper/2609.25466/) (2026)

## Abstract (arXiv)

We propose a Dynamic Graph-Based Spatial-Temporal Attention (DG-STA) method for hand gesture recognition. The key idea is to first construct a fully-connected graph from a hand skeleton, where the node features and edges are then automatically learned via a self-attention mechanism that performs in both spatial and temporal domains. We further propose to leverage the spatial-temporal cues of joint positions to guarantee robust recognition in challenging conditions. In addition, a novel spatial-temporal mask is applied to significantly cut down the computational cost by 99%. We carry out extensive experiments on benchmarks (DHG-14/28 and SHREC'17) and prove the superior performance of our method compared with the state-of-the-art methods. The source code can be found at https://github.com/yuxiaochen1103/DG-STA.