Papers › Consensus-based Optimization for 3D Human Pose Estimation in Camera Coordinates
Consensus-based Optimization for 3D Human Pose Estimation in Camera Coordinates
Diogo C. Luvizon, Hedi Tabia, David Picard
3D human pose estimation is frequently seen as the task of estimating 3D poses relative to the root body joint. Alternatively, we propose a 3D human pose estimation method in camera coordinates, which allows effective combination of 2D annotated data and 3D poses and a straightforward multi-view generalization. To that end, we cast the problem as a view frustum space pose estimation, where absolute depth prediction and joint relative depth estimations are disentangled. Final 3D predictions are obtained in camera coordinates by the inverse camera projection. Based on this, we also present a consensus-based optimization algorithm for multi-view predictions from uncalibrated images, which requires a single monocular training procedure. Although our method is indirectly tied to the training camera intrinsics, it still converges for cameras with different intrinsic parameters, resulting in coherent estimations up to a scale factor. Our method improves the state of the art on well known 3D human pose datasets, reducing the prediction error by 32% in the most common benchmark. We also reported our results in absolute pose position error, achieving 80~mm for monocular estimations and 51~mm for multi-view, on average.
Code
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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 | Pose Consensus (multi-view, GT calib.) | Average MPJPE (mm) | 39 | #16 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Pose Consensus (multi-view, GT calib.) | Multi-View or Monocular | Multi-View | #16 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Pose Consensus (multi-view, GT calib.) | Using 2D ground-truth joints | No | #16 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Pose Consensus (multi-view, est. calib.) | Average MPJPE (mm) | 45 | #43 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Pose Consensus (multi-view, est. calib.) | Multi-View or Monocular | Multi-View | #43 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Pose Consensus (multi-view, est. calib.) | Using 2D ground-truth joints | No | #43 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Pose Consensus (monocular) | Average MPJPE (mm) | 52 | #74 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Pose Consensus (monocular) | Multi-View or Monocular | Monocular | #74 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Pose Consensus (monocular) | Using 2D ground-truth joints | No | #74 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | Pose Consensus (monocular) | AUC | 42.1 | #77 of 108 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | Pose Consensus (monocular) | MPJPE | 112.1 | #77 of 108 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | Pose Consensus (monocular) | PCK | 80.6 | #77 of 108 | 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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