Papers › Domain and View-point Agnostic Hand Action Recognition
Domain and View-point Agnostic Hand Action Recognition
Alberto Sabater, Iñigo Alonso, Luis Montesano, Ana C. Murillo
Hand action recognition is a special case of action recognition with applications in human-robot interaction, virtual reality or life-logging systems. Building action classifiers able to work for such heterogeneous action domains is very challenging. There are very subtle changes across different actions from a given application but also large variations across domains (e.g. virtual reality vs life-logging). This work introduces a novel skeleton-based hand motion representation model that tackles this problem. The framework we propose is agnostic to the application domain or camera recording view-point. When working on a single domain (intra-domain action classification) our approach performs better or similar to current state-of-the-art methods on well-known hand action recognition benchmarks. And, more importantly, when performing hand action recognition for action domains and camera perspectives which our approach has not been trained for (cross-domain action classification), our proposed framework achieves comparable performance to intra-domain state-of-the-art methods. These experiments show the robustness and generalization capabilities of our framework.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Skeleton Based Action Recognition | First-Person Hand Action Benchmark | TCN-Summ | 1:1 Accuracy | 95.93 | #1 of 4 | Archive leaderboard | report |
| Skeleton Based Action Recognition | First-Person Hand Action Benchmark | TCN-Summ | 1:3 Accuracy | 92.9 | #1 of 4 | Archive leaderboard | report |
| Skeleton Based Action Recognition | First-Person Hand Action Benchmark | TCN-Summ | 3:1 Accuracy | 96.76 | #1 of 4 | Archive leaderboard | report |
| Skeleton Based Action Recognition | First-Person Hand Action Benchmark | TCN-Summ | Cross-person Accuracy | 88.70 | #1 of 4 | Archive leaderboard | report |
| Skeleton Based Action Recognition | SHREC 2017 track on 3D Hand Gesture Recognition | TCN-Summ | 14 gestures accuracy | 93.57 | #5 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | SHREC 2017 track on 3D Hand Gesture Recognition | TCN-Summ | 28 gestures accuracy | 91.43 | #5 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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