Papers › Stacked Hourglass Networks for Human Pose Estimation
Stacked Hourglass Networks for Human Pose Estimation
Alejandro Newell, Kaiyu Yang, Jia Deng
This work introduces a novel convolutional network architecture for the task of human pose estimation. Features are processed across all scales and consolidated to best capture the various spatial relationships associated with the body. We show how repeated bottom-up, top-down processing used in conjunction with intermediate supervision is critical to improving the performance of the network. We refer to the architecture as a "stacked hourglass" network based on the successive steps of pooling and upsampling that are done to produce a final set of predictions. State-of-the-art results are achieved on the FLIC and MPII benchmarks outcompeting all recent methods.
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Code
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46 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
25 samples harvested; 2 ran; 0 honoured the contract we drafted; 23 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Pose Estimation | FLIC Elbows | Stacked Hourglass Networks | PCK@0.2 | 99.0% | #1 of 2 | Archive leaderboard | report |
| Pose Estimation | FLIC Wrists | Stacked Hourglass Networks | PCK@0.2 | 97.0% | #1 of 2 | Archive leaderboard | report |
| Pose Estimation | MPII Human Pose | Stacked Hourglass Networks | PCKh-0.5 | 90.9 | #24 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.
Methods
Introduced by this paper: Stacked Hourglass Network
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