Papers › DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation
DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation
Markus Oberweger, Vincent Lepetit
DeepPrior is a simple approach based on Deep Learning that predicts the joint 3D locations of a hand given a depth map. Since its publication early 2015, it has been outperformed by several impressive works. Here we show that with simple improvements: adding ResNet layers, data augmentation, and better initial hand localization, we achieve better or similar performance than more sophisticated recent methods on the three main benchmarks (NYU, ICVL, MSRA) while keeping the simplicity of the original method. Our new implementation is available at https://github.com/moberweger/deep-prior-pp .
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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 |
|---|---|---|---|---|---|---|---|
| Hand Pose Estimation | ICVL Hands | DeepPrior++ | Average 3D Error | 8.1 | #15 of 15 | Archive leaderboard | report |
| Hand Pose Estimation | MSRA Hands | DeepPrior++ | Average 3D Error | 9.5 | #10 of 11 | Archive leaderboard | report |
| Hand Pose Estimation | NYU Hands | DeepPrior++ | Average 3D Error | 12.3 | #15 of 17 | 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.
Methods
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