Papers › Convolutional Pose Machines
Convolutional Pose Machines
Shih-En Wei, Varun Ramakrishna, Takeo Kanade, Yaser Sheikh
Pose Machines provide a sequential prediction framework for learning rich implicit spatial models. In this work we show a systematic design for how convolutional networks can be incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation. The contribution of this paper is to implicitly model long-range dependencies between variables in structured prediction tasks such as articulated pose estimation. We achieve this by designing a sequential architecture composed of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations, without the need for explicit graphical model-style inference. Our approach addresses the characteristic difficulty of vanishing gradients during training by providing a natural learning objective function that enforces intermediate supervision, thereby replenishing back-propagated gradients and conditioning the learning procedure. We demonstrate state-of-the-art performance and outperform competing methods on standard benchmarks including the MPII, LSP, and FLIC datasets.
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Code
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Code Syntology ran Syntology
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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 | Total Capture | Tri-CPM | Average MPJPE (mm) | 99 | #12 of 14 | Archive leaderboard | report |
| Car Pose Estimation | ApolloCar3D | CPM | Detection Rate | 75.4 | #3 of 3 | Archive leaderboard | report |
| Classification | RSSCN7 | CPM | 1:1 Accuracy | 50 | #2 of 2 | Archive leaderboard | report |
| Pose Estimation | FLIC Elbows | Convolutional Pose Machines | PCK@0.2 | 97.59% | #2 of 2 | Archive leaderboard | report |
| Pose Estimation | FLIC Wrists | Convolutional Pose Machines | PCK@0.2 | 95.03% | #2 of 2 | Archive leaderboard | report |
| Pose Estimation | J-HMDB | CPM | Mean PCK@0.2 | 91.9 | #4 of 5 | Archive leaderboard | report |
| Pose Estimation | Leeds Sports Poses | Convolutional Pose Machines | PCK | 90.5% | #11 of 18 | Archive leaderboard | report |
| Pose Estimation | MPII Human Pose | Convolutional Pose Machines | PCKh-0.5 | 88.52 | #32 of 46 | 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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