# DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition

arXiv: https://arxiv.org/abs/2210.05895 · HTML: https://mlsanity.com/paper/2210.05895/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 61 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 30 of 45)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2107.12213/) (2021)
- [Video Classification with Channel-Separated Convolutional Networks](https://mlsanity.com/paper/1904.02811/) (2019)
- [BABEL: Bodies, Action and Behavior with English Labels](https://mlsanity.com/paper/2106.09696/) (2021)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [Deep Convolutional Networks on Graph-Structured Data](https://mlsanity.com/paper/1506.05163/) (2015)
- [Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/1904.12659/) (2019)
- [Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition](https://mlsanity.com/paper/1607.07043/) (2016)
- [Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition](https://mlsanity.com/paper/1904.01189/) (2019)
- [An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data](https://mlsanity.com/paper/1611.06067/) (2016)
- [A New Representation of Skeleton Sequences for 3D Action Recognition](https://mlsanity.com/paper/1703.03492/) (2017)
- [Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation](https://mlsanity.com/paper/1804.06055/) (2018)

## Built on by (most cited first, 26 of 26)

- [Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition](https://mlsanity.com/paper/2411.18941/) (2024)
- [Joint Skeletal and Semantic Embedding Loss for Micro-gesture Classification](https://mlsanity.com/paper/2307.10624/) (2023)
- [Prototype Learning for Micro-gesture Classification](https://mlsanity.com/paper/2408.03097/) (2024)
- [Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language](https://mlsanity.com/paper/2305.15842/) (2023)
- [Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding](https://mlsanity.com/paper/2405.13206/) (2024)
- [Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition](https://mlsanity.com/paper/2308.07571/) (2023)
- [An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition](https://mlsanity.com/paper/2406.00639/) (2024)
- [Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey](https://mlsanity.com/paper/2310.13039/) (2023)
- [Expressive Keypoints for Skeleton-based Action Recognition via Skeleton Transformation](https://mlsanity.com/paper/2406.18011/) (2024)
- [VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG](https://mlsanity.com/paper/2311.14775/) (2023)
- [Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?](https://mlsanity.com/paper/2505.10679/) (2025)
- [STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton Sequences](https://mlsanity.com/paper/2407.10935/) (2024)
- [SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature Aggregation](https://mlsanity.com/paper/2504.11749/) (2025)
- [Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence](https://mlsanity.com/paper/2401.00921/) (2024)
- [Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning](https://mlsanity.com/paper/2407.01397/) (2024)
- [BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports](https://mlsanity.com/paper/2502.21085/) (2025)
- [Adversarially-Refined VQ-GAN with Dense Motion Tokenization for Spatio-Temporal Heatmaps](https://mlsanity.com/paper/2509.19252/) (2025)
- [IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare](https://mlsanity.com/paper/2501.07039/) (2025)
- [SkeletonAgent: An Agentic Interaction Framework for Skeleton-based Action Recognition](https://mlsanity.com/paper/2511.22433/) (2025)
- [Multi-Track Multimodal Learning on iMiGUE: Micro-Gesture and Emotion Recognition](https://mlsanity.com/paper/2512.23291/) (2025)
- [Interact with me: Joint Egocentric Forecasting of Intent to Interact, Attitude and Social Actions](https://mlsanity.com/paper/2412.16698/) (2024)
- [SkeFi: Cross-Modal Knowledge Transfer for Wireless Skeleton-Based Action Recognition](https://mlsanity.com/paper/2601.12432/) (2026)
- [Affinity Contrastive Learning for Skeleton-based Human Activity Understanding](https://mlsanity.com/paper/2601.16694/) (2026)
- [Skarimva: Skeleton-based Action Recognition is a Multi-view Application](https://mlsanity.com/paper/2602.23231/) (2026)
- [Recognition of Daily Activities through Multi-Modal Deep Learning: A Video, Pose, and Object-Aware Approach for Ambient Assisted Living](https://mlsanity.com/paper/2603.04509/) (2026)
- [SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities](https://mlsanity.com/paper/2609.08038/) (2026)

## Abstract (arXiv)

Graph convolution networks (GCN) have been widely used in skeleton-based action recognition. We note that existing GCN-based approaches primarily rely on prescribed graphical structures (ie., a manually defined topology of skeleton joints), which limits their flexibility to capture complicated correlations between joints. To move beyond this limitation, we propose a new framework for skeleton-based action recognition, namely Dynamic Group Spatio-Temporal GCN (DG-STGCN). It consists of two modules, DG-GCN and DG-TCN, respectively, for spatial and temporal modeling. In particular, DG-GCN uses learned affinity matrices to capture dynamic graphical structures instead of relying on a prescribed one, while DG-TCN performs group-wise temporal convolutions with varying receptive fields and incorporates a dynamic joint-skeleton fusion module for adaptive multi-level temporal modeling. On a wide range of benchmarks, including NTURGB+D, Kinetics-Skeleton, BABEL, and Toyota SmartHome, DG-STGCN consistently outperforms state-of-the-art methods, often by a notable margin.