Papers › NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle...
NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle Human Actions
Neel Trivedi, Anirudh Thatipelli, Ravi Kiran Sarvadevabhatla
The lack of fine-grained joints (facial joints, hand fingers) is a fundamental performance bottleneck for state of the art skeleton action recognition models. Despite this bottleneck, community's efforts seem to be invested only in coming up with novel architectures. To specifically address this bottleneck, we introduce two new pose based human action datasets - NTU60-X and NTU120-X. Our datasets extend the largest existing action recognition dataset, NTU-RGBD. In addition to the 25 body joints for each skeleton as in NTU-RGBD, NTU60-X and NTU120-X dataset includes finger and facial joints, enabling a richer skeleton representation. We appropriately modify the state of the art approaches to enable training using the introduced datasets. Our results demonstrate the effectiveness of these NTU-X datasets in overcoming the aforementioned bottleneck and improve state of the art performance, overall and on previously worst performing action categories. Code and pretrained models can be found at https://github.com/skelemoa/ntu-x .
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Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Skeleton Based Action Recognition | NTU60-X | 4s-ShiftGCN | Accuracy (Body + Fingers + Face joints) | 89.64 | #1 of 3 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU60-X | 4s-ShiftGCN | Accuracy (Body + Fingers joints) | 91.78 | #1 of 3 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU60-X | 4s-ShiftGCN | Accuracy (Body joints) | 89.56 | #1 of 3 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU60-X | MS-G3D | Accuracy (Body + Fingers + Face joints) | 91.12 | #2 of 3 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU60-X | MS-G3D | Accuracy (Body + Fingers joints) | 91.76 | #2 of 3 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU60-X | MS-G3D | Accuracy (Body joints) | 91.26 | #2 of 3 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU60-X | PA-ResGCN | Accuracy (Body + Fingers + Face joints) | 89.79 | #3 of 3 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU60-X | PA-ResGCN | Accuracy (Body + Fingers joints) | 91.64 | #3 of 3 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU60-X | PA-ResGCN | Accuracy (Body joints) | 89.98 | #3 of 3 | 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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