Research map / 3D vision / Human pose estimation
Feature Mapping for Learning Fast and Accurate 3D Pose Inference From Synthetic Images
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| map real-image features into the synthetic feature space before pose prediction | Feature Mapping · LINEMOD | 2D Projection: 90.8 -> 93.4 | Helped |
| use feature mapping with the pose-refinement stage | Feature Mapping · LINEMOD | 2D Projection: 91.8 -> 95.4 | Helped |
| use feature mapping with the pose-refinement stage | Feature Mapping · LINEMOD | 6D pose: 76.6 -> 78.7 | Helped |
| use feature mapping with the pose-refinement stage | Feature Mapping · LINEMOD | 5cm 5°: 73.3 -> 80.1 | Helped |
| map real depth-image features to synthetic features instead of training on synthetic depth maps only (ablation) | 3D hand pose model · NYU | Average 3D error: 21.1mm -> 7.4mm | Helped |
| use feature mapping rather than the strongest listed baseline | 3D hand pose model · NYU | Average 3D error: 12.3mm -> 7.4mm | Helped |
| train on synthetic depth maps only, omitting real-to-synthetic feature mapping (ablation) | 3D hand pose model · NYU | Average 3D error: 7.4mm -> 21.1mm | Hurt |
Closest papers by meaning
- Sim2real transfer learning for 3D human pose estimation: motion to the rescue (NeurIPS 2019 · similarity 0.88)
- Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis (NeurIPS 2024 · similarity 0.88)
- DualPM: Dual Posed-Canonical Point Maps for 3D Shape and Pose Reconstruction (CVPR 2025 · similarity 0.87)
- LEAP: Liberate Sparse-View 3D Modeling from Camera Poses (ICLR 2024 · similarity 0.87)
- ImageNet3D: Towards General-Purpose Object-Level 3D Understanding (NeurIPS 2024 · similarity 0.86)
- NOPE: Novel Object Pose Estimation from a Single Image (CVPR 2024 · similarity 0.86)
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