Methods › General › Action Recognition Blocks › G3D
G3D
Introduced by Ziyu Liu et al. in Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
G3D is a unified spatial-temporal graph convolutional operator that directly models cross-spacetime joint dependencies. It leverages dense cross-spacetime edges as skip connections for direct information propagation across the 3D spatial-temporal graph.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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HVIS: A Human-like Vision and Inference System for Human Motion Prediction 24 Feb 2025 · 0 repositories · arXiv:2502.16913
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Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition 31 Mar 2020 · 3 repositories · arXiv:2003.14111Syntology ran 3 of 3 samples · 0 unverified
Tasks archive 2025-07-28
7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| 3D Action Recognition | 1 |
| Action Recognition | 1 |
| Human motion prediction | 1 |
| Long-range modeling | 1 |
| Navigate | 1 |
| Skeleton Based Action Recognition | 1 |
| motion prediction | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections