EgoEV-HandPose: Egocentric 3D Hand Pose Estimation and Gesture Recognition with Stereo Event Cameras
Overview
This paper introduces EgoEV-HandPose, a framework for joint 3D bimanual hand pose estimation and gesture recognition using stereo event streams. The core KeypointBEV module combines sparse 3D joint hypotheses with geometry-aware stereo fusion and iterative reprojection-guided refinement to resolve depth uncertainty. The authors also release EgoEVHands, the first large-scale real-world stereo event dataset for this task, containing 2,498 annotated stereo sequence pairs across 38 gesture classes. Experiments show a mean per-joint position error (MPJPE) of 30.54mm and gesture recognition accuracy of 86.87%, outperforming RGB-based stereo and prior event-based methods, with demonstrated robustness under low illumination and severe occlusion.
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Reported quantity · trajectories: 2498 other · trajectory count
“real-world stereo event dataset for egocentric 3D bimanual hand pose estimation and gesture recognition. Built upon EgoEvGesture recordings, it adds dense 2D/3D hand annotations and comprises 2,498 annotated stereo sequence pairs across 38 gesture classes. Extensive experiments demonstrate that EgoEV-HandPose achieves state-of-the-art performance on EgoEVHands, obtaining a mean per-joint position error (MPJPE) of 30”
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Reported quantity · percent: 86.87 other · Basis not reported
“.54mm and a gesture recognition accuracy of 86.87%, significantly outperforming RGB-based stereo and previous event-based methods. The framework exhibits exceptional robustness under low illumination and severe bimanual occlusion. Experiments on DHP19 further demonstrate the cross-dataset and cross-architecture generalizability of the KeypointBEV refinement design. Dataset and code will be released at https://github.”
Exact source · revision 1Source owner not reported
Artifact availability · code: reported pending
“.54mm and a gesture recognition accuracy of 86.87%, significantly outperforming RGB-based stereo and previous event-based methods. The framework exhibits exceptional robustness under low illumination and severe bimanual occlusion. Experiments on DHP19 further demonstrate the cross-dataset and cross-architecture generalizability of the KeypointBEV refinement design. Dataset and code will be released at https://github.”
Exact source · revision 1Source owner not reported
Artifact availability · dataset: reported pending
“.54mm and a gesture recognition accuracy of 86.87%, significantly outperforming RGB-based stereo and previous event-based methods. The framework exhibits exceptional robustness under low illumination and severe bimanual occlusion. Experiments on DHP19 further demonstrate the cross-dataset and cross-architecture generalizability of the KeypointBEV refinement design. Dataset and code will be released at https://github.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalEgoEV-HandPose: Egocentric 3D Hand Pose Estimation and Gesture Recognition with Stereo Event Cameras
This paper introduces EgoEV-HandPose, a framework for joint 3D bimanual hand pose estimation and gesture recognition using stereo event streams. The core KeypointBEV module combines sparse 3D joint hypotheses with geometry-aware stereo fusion and iterative reprojection-guided refinement to resolve depth uncertainty. The authors also release EgoEVHands, the first large-scale real-world stereo event dataset for this task, containing 2,498 annotated stereo sequence pairs across 38 gesture classes. Experiments show a mean per-joint position error (MPJPE) of 30.54mm and gesture recognition accuracy of 86.87%, outperforming RGB-based stereo and prior event-based methods, with demonstrated robustness under low illumination and severe occlusion.
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