PoseRN: A 2D pose refinement network for bias-free multi-view 3D human pose estimation
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Abstract (arXiv)
We propose a new 2D pose refinement network that learns to predict the human bias in the estimated 2D pose. There are biases in 2D pose estimations that are due to differences between annotations of 2D joint locations based on annotators' perception and those defined by motion capture (MoCap) systems. These biases are crafted into publicly available 2D pose datasets and cannot be removed with existing error reduction approaches. Our proposed pose refinement network allows us to efficiently remove the human bias in the estimated 2D poses and achieve highly accurate multi-view 3D human pose estimation.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 17 references that are in the map. First posted on arXiv 7 Jul 2021.
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References · 13
- 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,161
- 2019Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loopcited by 625
- 2016Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervisioncited by 493
- 2017A simple yet effective baseline for 3d human pose estimationcited by 401
- 2017Integral Human Pose Regressioncited by 255
- 2016Panoptic Studio: A Massively Multiview System for Social Interaction Capturecited by 173
- 2019Learnable Triangulation of Human Posecited by 160
- 2017Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approachcited by 114
- 2019Cross View Fusion for 3D Human Pose Estimationcited by 90
- 2019HEMlets Pose: Learning Part-Centric Heatmap Triplets for Accurate 3D Human Pose Estimationcited by 50
- 2017Harvesting Multiple Views for Marker-less 3D Human Pose Annotationscited by 48
- 20183D Human Pose Estimation with 2D Marginal Heatmapscited by 20
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