Papers › Shape-Aware Human Pose and Shape Reconstruction Using Multi-View Images
Shape-Aware Human Pose and Shape Reconstruction Using Multi-View Images
Junbang Liang, Ming C. Lin
We propose a scalable neural network framework to reconstruct the 3D mesh of a human body from multi-view images, in the subspace of the SMPL model. Use of multi-view images can significantly reduce the projection ambiguity of the problem, increasing the reconstruction accuracy of the 3D human body under clothing. Our experiments show that this method benefits from the synthetic dataset generated from our pipeline since it has good flexibility of variable control and can provide ground-truth for validation. Our method outperforms existing methods on real-world images, especially on shape estimations.
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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 | Shape-aware SMPL | Average MPJPE (mm) | 44.4 | #38 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Shape-aware SMPL | Multi-View or Monocular | Multi-View | #38 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Shape-aware SMPL | Using 2D ground-truth joints | No | #38 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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