Papers › Learning to Refine Human Pose Estimation
Learning to Refine Human Pose Estimation
Mihai Fieraru, Anna Khoreva, Leonid Pishchulin, Bernt Schiele
Multi-person pose estimation in images and videos is an important yet challenging task with many applications. Despite the large improvements in human pose estimation enabled by the development of convolutional neural networks, there still exist a lot of difficult cases where even the state-of-the-art models fail to correctly localize all body joints. This motivates the need for an additional refinement step that addresses these challenging cases and can be easily applied on top of any existing method. In this work, we introduce a pose refinement network (PoseRefiner) which takes as input both the image and a given pose estimate and learns to directly predict a refined pose by jointly reasoning about the input-output space. In order for the network to learn to refine incorrect body joint predictions, we employ a novel data augmentation scheme for training, where we model "hard" human pose cases. We evaluate our approach on four popular large-scale pose estimation benchmarks such as MPII Single- and Multi-Person Pose Estimation, PoseTrack Pose Estimation, and PoseTrack Pose Tracking, and report systematic improvement over the state of the art.
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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 | MPII Multi-Person | Refine | mAP@0.5 | 78% | #4 of 9 | Archive leaderboard | report |
| Multi-Person Pose Estimation | MPII Multi-Person | Refine | AP | 78% | #4 of 9 | Archive leaderboard | report |
| Multi-Person Pose Estimation | PoseTrack2018 | Refine | Mean mAP | 73.8 | #4 of 4 | Archive leaderboard | report |
| Multi-Person Pose Estimation and Tracking | PoseTrack2018 | Refine | MOTA | 58.4 | #1 of 1 | Archive leaderboard | report |
| Pose Estimation | MPII Single Person | Refine | PCKh@0.5 | 92.1 | #2 of 5 | 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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