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

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arxiv 2210.05895 v1 pith:NGJ5K6CE submitted 2022-10-12 cs.CV

Haodong Duan , Jiaqi Wang , Kai Chen , Dahua Lin This is my paper · ORCID
classification cs.CV
keywords dynamicactiondg-stgcnmodelingrecognitionskeleton-basedtemporalcapture
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Interact with me: Joint Egocentric Forecasting of Intent to Interact, Attitude and Social Actions

    cs.CV 2024-12 conditional novelty 6.0 of 10

    SocialEgoNet jointly forecasts interaction intent, attitude, and actions from one second of egocentric skeleton video on a newly labeled version of the JPL-Interaction dataset.

  2. Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition

    cs.CV 2024-11 conditional novelty 6.0 of 10

    ProtoGCN, a GCN with a prototype reconstruction memory and class-specific contrastive loss, achieves state-of-the-art accuracy on NTU-60, NTU-120, Kinetics-Skeleton, and FineGYM.

  3. DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology Modeling

    cs.CV 2025-01 conditional novelty 5.0 of 10

    A graph neural network that learns separate joint-connection patterns for each feature channel and each time frame reports top or near-top accuracy on four skeleton gesture and action benchmarks.

  4. Evolving Skeletons: Motion Dynamics in Action Recognition

    cs.CV 2025-01 conditional novelty 5.0 of 10

    Taylor-transformed skeletons improve ST-GCN accuracy but reduce Hyperformer accuracy on NTU-60/120, indicating that motion-injected inputs do not universally benefit skeleton-based action recognition models.

  5. SMART-Vision: Survey of Modern Action Recognition Techniques in Vision

    cs.CV 2025-01 conditional novelty 4.0 of 10

    The SMART-Vision survey organizes vision-based human action recognition into a hybrid Venn-diagram taxonomy and reviews the emerging open-set/open-world HAR literature.

  6. 3D Skeleton-Based Action Recognition: A Review

    cs.CV 2025-06 reject novelty 3.0 of 10

    A task-oriented review of skeleton-based action recognition that reorganizes known methods along a data processing pipeline and contains no new experimental result.

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