Papers › VoxelKeypointFusion: Generalizable Multi-View Multi-Person Pose Estimation
VoxelKeypointFusion: Generalizable Multi-View Multi-Person Pose Estimation
Daniel Bermuth, Alexander Poeppel, Wolfgang Reif
In the rapidly evolving field of computer vision, the task of accurately estimating the poses of multiple individuals from various viewpoints presents a formidable challenge, especially if the estimations should be reliable as well. This work presents an extensive evaluation of the generalization capabilities of multi-view multi-person pose estimators to unseen datasets and presents a new algorithm with strong performance in this task. It also studies the improvements by additionally using depth information. Since the new approach can not only generalize well to unseen datasets, but also to different keypoints, the first multi-view multi-person whole-body estimator is presented. To support further research on those topics, all of the work is publicly accessible.
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 | VoxelKeypointFusion (transfer) | Average MPJPE (mm) | 64.3 | #86 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | VoxelKeypointFusion (transfer) | Multi-View or Monocular | Multi-View | #86 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | VoxelKeypointFusion (transfer) | Using 2D ground-truth joints | No | #86 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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