Research map / 3D vision / Human pose estimation
Lifting From the Deep: Convolutional 3D Pose Estimation From a Single Image
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use a mixture of PPCA pose models instead of a single PPCA model (ablation) | Ours · Human3.6M Protocol #1 | 3D error (mm): 92.96 -> 88.39 | Helped |
| add 3D pose refinement to the trained CPM architecture | CPM · Human3.6M | 2D pixel error: 10.04 -> 9.47 | Helped |
| use the proposed mixture-PPCA method instead of the strongest listed baseline | 3D pose estimation · Human3.6M Protocol #1 | 3D error (mm): 93.15 -> 88.39 | Helped |
| use the proposed method instead of Rogez et al. | 3D pose estimation · Human3.6M Protocol #2 | 3D pose error (mm): 88.1 -> 70.7 | Helped |
| use the proposed method instead of Bogo et al. | 3D pose estimation · Human3.6M Protocol #3 | 3D pose error (mm): 82.3 -> 79.6 | Helped |
Closest papers by meaning
- 3D Human Pose Estimation = 2D Pose Estimation + Matching (CVPR 2017 · similarity 0.94)
- PandaPose: 3D Human Pose Lifting from a Single Image via Propagating 2D Pose Prior to 3D Anchor Space (NeurIPS 2025 · similarity 0.89)
- PoseIRM: Enhance 3D Human Pose Estimation on Unseen Camera Settings via Invariant Risk Minimization (CVPR 2024 · similarity 0.88)
- Lifting by Image – Leveraging Image Cues for Accurate 3D Human Pose Estimation (AAAI 2024 · similarity 0.88)
- Deep Semantic Graph Transformer for Multi-View 3D Human Pose Estimation (AAAI 2024 · similarity 0.88)
- ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation (NeurIPS 2024 · similarity 0.88)
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