Papers › Cross View Fusion for 3D Human Pose Estimation
Cross View Fusion for 3D Human Pose Estimation
Haibo Qiu, Chunyu Wang, Jingdong Wang, Naiyan Wang, Wen-Jun Zeng
We present an approach to recover absolute 3D human poses from multi-view images by incorporating multi-view geometric priors in our model. It consists of two separate steps: (1) estimating the 2D poses in multi-view images and (2) recovering the 3D poses from the multi-view 2D poses. First, we introduce a cross-view fusion scheme into CNN to jointly estimate 2D poses for multiple views. Consequently, the 2D pose estimation for each view already benefits from other views. Second, we present a recursive Pictorial Structure Model to recover the 3D pose from the multi-view 2D poses. It gradually improves the accuracy of 3D pose with affordable computational cost. We test our method on two public datasets H36M and Total Capture. The Mean Per Joint Position Errors on the two datasets are 26mm and 29mm, which outperforms the state-of-the-arts remarkably (26mm vs 52mm, 29mm vs 35mm). Our code is released at \url{https://github.com/microsoft/multiview-human-pose-estimation-pytorch}.
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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 | Fusion-RPSM (t=10) | Average MPJPE (mm) | 31.17 | #11 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Fusion-RPSM (t=10) | Multi-View or Monocular | Multi-View | #11 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Fusion-RPSM (t=10) | Using 2D ground-truth joints | No | #11 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Total Capture | Fusion-RPSM | Average MPJPE (mm) | 29.0 | #6 of 14 | Archive leaderboard | report |
| 3D Human Pose Estimation | Total Capture | Single-RPSM | Average MPJPE (mm) | 41.0 | #10 of 14 | 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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