Papers › PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up,...
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
George Papandreou, Tyler Zhu, Liang-Chieh Chen, Spyros Gidaris, Jonathan Tompson, Kevin Murphy
We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model. The proposed PersonLab model tackles both semantic-level reasoning and object-part associations using part-based modeling. Our model employs a convolutional network which learns to detect individual keypoints and predict their relative displacements, allowing us to group keypoints into person pose instances. Further, we propose a part-induced geometric embedding descriptor which allows us to associate semantic person pixels with their corresponding person instance, delivering instance-level person segmentations. Our system is based on a fully-convolutional architecture and allows for efficient inference, with runtime essentially independent of the number of people present in the scene. Trained on COCO data alone, our system achieves COCO test-dev keypoint average precision of 0.665 using single-scale inference and 0.687 using multi-scale inference, significantly outperforming all previous bottom-up pose estimation systems. We are also the first bottom-up method to report competitive results for the person class in the COCO instance segmentation task, achieving a person category average precision of 0.417.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Keypoint Detection | COCO (Common Objects in Context) | PersonLab | Test AP | 66.5 | #14 of 24 | Archive leaderboard | report |
| Multi-Person Pose Estimation | COCO test-dev | PersonLab | AP | 68.7 | #8 of 15 | Archive leaderboard | report |
| Multi-Person Pose Estimation | COCO test-dev | PersonLab | AP50 | 89.0 | #8 of 15 | Archive leaderboard | report |
| Multi-Person Pose Estimation | COCO test-dev | PersonLab | AP75 | 75.4 | #8 of 15 | Archive leaderboard | report |
| Multi-Person Pose Estimation | COCO test-dev | PersonLab | APL | 75.5 | #8 of 15 | Archive leaderboard | report |
| Multi-Person Pose Estimation | COCO test-dev | PersonLab | APM | 64.1 | #8 of 15 | 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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