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Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

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arxiv 1801.07455 v2 pith:HCUXUSFX submitted 2018-01-23 cs.CV

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

Sijie Yan , Yuanjun Xiong , Dahua Lin This is my paper
classification cs.CV
keywords skeletonsactionconvolutionalexpressivegeneralizationgraphhumanmethods
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties of generalization. In this work, we propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data. This formulation not only leads to greater expressive power but also stronger generalization capability. On two large datasets, Kinetics and NTU-RGBD, it achieves substantial improvements over mainstream methods.

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Cited by 4 Pith papers

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    A physics-based self-supervised autoencoder achieves label-free IMU tracking and motion capture that outperforms supervised baselines in generalization tests.

  3. StormNet: Improving storm surge predictions with a GNN-based spatio-temporal offset forecasting model

    cs.LG 2026-04 conditional novelty 6.0

    A spatio-temporal GNN model reduces storm surge water-level forecast RMSE by more than 70% for 48-hour horizons and over 50% for 72-hour horizons on U.S. Gulf Coast hurricane data.

  4. CascadeFormer: A Family of Two-stage Cascading Transformers for Skeleton-based Human Action Recognition

    cs.CV 2025-08 conditional novelty 4.0

    A masked-pretrained skeleton transformer with a second fine-tuning transformer and cross-attention fusion reaches 94.66% on Penn Action, 91.16% on N-UCLA, and 81.01%/88.17% on NTU RGB+D 60 cross-subject/cross-view.