# Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition

arXiv: https://arxiv.org/abs/1802.09834 · HTML: https://mlsanity.com/paper/1802.09834/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 15 of 15)

- [iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis](https://mlsanity.com/paper/2107.00285/) (2021)
- [SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos](https://mlsanity.com/paper/2104.11452/) (2021)
- [GeomNet: A Neural Network Based on Riemannian Geometries of SPD Matrix Space and Cholesky Space for 3D Skeleton-Based Interaction Recognition](https://mlsanity.com/paper/2111.13089/) (2021)
- [Dynamic Graph Representation Learning with Neural Networks: A Survey](https://mlsanity.com/paper/2304.05729/) (2023)
- [InfoGCN++: Learning Representation by Predicting the Future for Online Human Skeleton-based Action Recognition](https://mlsanity.com/paper/2310.10547/) (2023)
- [Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding](https://mlsanity.com/paper/2405.13206/) (2024)
- [Graph Neural Network for spatiotemporal data: methods and applications](https://mlsanity.com/paper/2306.00012/) (2023)
- [Action Recognition with Domain Invariant Features of Skeleton Image](https://mlsanity.com/paper/2111.11250/) (2021)
- [Live Stream Temporally Embedded 3D Human Body Pose and Shape Estimation](https://mlsanity.com/paper/2207.12537/) (2022)
- [ChildPlay-Hand: A Dataset of Hand Manipulations in the Wild](https://mlsanity.com/paper/2409.09319/) (2024)
- [MutualGraphNet: A novel model for motor imagery classification](https://mlsanity.com/paper/2109.04361/) (2021)
- [Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.05482/) (2021)
- [One-Shot Multi-Rate Pruning of Graph Convolutional Networks](https://mlsanity.com/paper/2312.17615/) (2023)
- [Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition](https://mlsanity.com/paper/2412.11813/) (2024)
- [Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting](https://mlsanity.com/paper/2508.00884/) (2025)

## Abstract (arXiv)

Variations of human body skeletons may be considered as dynamic graphs, which are generic data representation for numerous real-world applications. In this paper, we propose a spatio-temporal graph convolution (STGC) approach for assembling the successes of local convolutional filtering and sequence learning ability of autoregressive moving average. To encode dynamic graphs, the constructed multi-scale local graph convolution filters, consisting of matrices of local receptive fields and signal mappings, are recursively performed on structured graph data of temporal and spatial domain. The proposed model is generic and principled as it can be generalized into other dynamic models. We theoretically prove the stability of STGC and provide an upper-bound of the signal transformation to be learnt. Further, the proposed recursive model can be stacked into a multi-layer architecture. To evaluate our model, we conduct extensive experiments on four benchmark skeleton-based action datasets, including the large-scale challenging NTU RGB+D. The experimental results demonstrate the effectiveness of our proposed model and the improvement over the state-of-the-art.