Papers › Absolute Human Pose Estimation with Depth Prediction Network
Absolute Human Pose Estimation with Depth Prediction Network
Márton Véges, András Lőrincz
The common approach to 3D human pose estimation is predicting the body joint coordinates relative to the hip. This works well for a single person but is insufficient in the case of multiple interacting people. Methods predicting absolute coordinates first estimate a root-relative pose then calculate the translation via a secondary optimization task. We propose a neural network that predicts joints in a camera centered coordinate system instead of a root-relative one. Unlike previous methods, our network works in a single step without any post-processing. Our network beats previous methods on the MuPoTS-3D dataset and achieves state-of-the-art results.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Multi-Person Pose Estimation (absolute) | MuPoTS-3D | Depth Prediction Network | MPJPE | 292 | #14 of 14 | Archive leaderboard | report |
| 3D Multi-Person Pose Estimation (root-relative) | MuPoTS-3D | Depth Prediction Network | MPJPE | 120 | #18 of 20 | Archive leaderboard | report |
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