Papers › Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection
Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection
Hang Ye, Wentao Zhu, Chunyu Wang, Rujie Wu, Yizhou Wang
While the voxel-based methods have achieved promising results for multi-person 3D pose estimation from multi-cameras, they suffer from heavy computation burdens, especially for large scenes. We present Faster VoxelPose to address the challenge by re-projecting the feature volume to the three two-dimensional coordinate planes and estimating X, Y, Z coordinates from them separately. To that end, we first localize each person by a 3D bounding box by estimating a 2D box and its height based on the volume features projected to the xy-plane and z-axis, respectively. Then for each person, we estimate partial joint coordinates from the three coordinate planes separately which are then fused to obtain the final 3D pose. The method is free from costly 3D-CNNs and improves the speed of VoxelPose by ten times and meanwhile achieves competitive accuracy as the state-of-the-art methods, proving its potential in real-time applications.
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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 | Campus | Faster VoxelPose | PCP3D | 96.9 | #5 of 16 | Archive leaderboard | report |
| 3D Multi-Person Pose Estimation | Panoptic | Faster VoxelPose | Average MPJPE (mm) | 18.41 | #6 of 20 | Archive leaderboard | report |
| 3D Multi-Person Pose Estimation | Shelf | Faster VoxelPose | PCP3D | 97.6 | #10 of 27 | 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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