Papers › Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation

Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation

11 Aug 2017arXiv:1708.03416archive 2025-07-28

Xinghao Chen, Guijin Wang, Hengkai Guo, Cairong Zhang

Hand pose estimation from a single depth image is an essential topic in computer vision and human computer interaction. Despite recent advancements in this area promoted by convolutional neural network, accurate hand pose estimation is still a challenging problem. In this paper we propose a Pose guided structured Region Ensemble Network (Pose-REN) to boost the performance of hand pose estimation. The proposed method extracts regions from the feature maps of convolutional neural network under the guide of an initially estimated pose, generating more optimal and representative features for hand pose estimation. The extracted feature regions are then integrated hierarchically according to the topology of hand joints by employing tree-structured fully connections. A refined estimation of hand pose is directly regressed by the proposed network and the final hand pose is obtained by utilizing an iterative cascaded method. Comprehensive experiments on public hand pose datasets demonstrate that our proposed method outperforms state-of-the-art algorithms.

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xinghaochen/Pose-REN officialmentioned on GitHubcaffe2 report

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Tasks

Hand Pose EstimationPose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Hand Pose Estimation HANDS 2017 Pose-REN Average 3D Error 11.70 #8 of 9 Archive leaderboard report
Hand Pose Estimation ICVL Hands Pose-REN Average 3D Error 6.8 #10 of 15 Archive leaderboard report
Hand Pose Estimation MSRA Hands Pose-REN Average 3D Error 8.6 #9 of 11 Archive leaderboard report
Hand Pose Estimation NYU Hands Pose-REN Average 3D Error 11.8 #14 of 17 Archive leaderboard report

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