Papers › PoseRN: A 2D pose refinement network for bias-free multi-view 3D human pose estimation
PoseRN: A 2D pose refinement network for bias-free multi-view 3D human pose estimation
Akihiko Sayo, Diego Thomas, Hiroshi Kawasaki, Yuta Nakashima, Katsushi Ikeuchi
We propose a new 2D pose refinement network that learns to predict the human bias in the estimated 2D pose. There are biases in 2D pose estimations that are due to differences between annotations of 2D joint locations based on annotators' perception and those defined by motion capture (MoCap) systems. These biases are crafted into publicly available 2D pose datasets and cannot be removed with existing error reduction approaches. Our proposed pose refinement network allows us to efficiently remove the human bias in the estimated 2D poses and achieve highly accurate multi-view 3D human pose estimation.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Human Pose Estimation | Human3.6M | PoseRN | Average MPJPE (mm) | 38.4 | #14 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | PoseRN | Multi-View or Monocular | Multi-View | #14 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | PoseRN | Using 2D ground-truth joints | No | #14 of 88 | 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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