← 研踪 PaperTrailarXiv:2605.12297 · v2
EgoEV-HandPose: Egocentric 3D Hand Pose Estimation and Gesture Recognition with Stereo Event Cameras
首次提交 2026-05-12 23:51
版本更新 2026-11-07 10:30
雷达观测 2026-11-10 10:11
摘要
Egocentric 3D hand pose estimation and gesture recognition are fundamental to understanding hand interactions from first-person observations. Robust perception remains challenging: frame-based cameras suffer from motion blur and limited dynamic range, while event-based approaches face ego-motion interference, monocular depth ambiguity, and limited real-world datasets. We propose EgoEV-HandPose, an end-to-end framework for joint 3D bimanual hand pose estimation and gesture recognition from stereo event streams. Its core module, KeypointBEV, combines sparse 3D joint hypotheses with geometry-aware stereo fusion and iterative reprojection-guided refinement to progressively resolve depth uncertainty and improve pose accuracy. We also introduce EgoEVHands, the first large-scale 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 30.54mm 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.com/ZJUWang01/EgoEV-HandPose.
作者备注：The dataset and code will be released at https://github.com/ZJUWang01/EgoEV-HandPose
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v2 · 2026-11-07 10:30 · 最新观测版本
EgoEV-HandPose: Egocentric 3D Hand Pose Estimation and Gesture Recognition with Stereo Event Cameras
Egocentric 3D hand pose estimation and gesture recognition are fundamental to understanding hand interactions from first-person observations. Robust perception remains challenging: frame-based cameras suffer from motion blur and limited dynamic range, while event-based approaches face ego-motion interference, monocular depth ambiguity, and limited real-world datasets. We propose EgoEV-HandPose, an end-to-end framework for joint 3D bimanual hand pose estimation and gesture recognition from stereo event streams. Its core module, KeypointBEV, combines sparse 3D joint hypotheses with geometry-aware stereo fusion and iterative reprojection-guided refinement to progressively resolve depth uncertainty and improve pose accuracy. We also introduce EgoEVHands, the first large-scale 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 30.54mm 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.com/ZJUWang01/EgoEV-HandPose.
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