Event camera shows great potential in 3D hand pose estimation, especially addressing the challenges of fast motion and high dynamic range in a low-power way. However, due to the asynchronous differential imaging mechanism, it is challenging to design event representation to encode hand motion information especially when the hands are not moving (causing motion ambiguity), and it is infeasible to fully annotate the temporally dense event stream. In this paper, we propose EvHandPose with novel hand flow representations in Event-to-Pose module for accurate hand pose estimation and alleviating the motion ambiguity issue. To solve the problem under sparse annotation, we design contrast maximization and edge constraints in Pose-to-IWE (Image with Warped Events) module and formulate EvHandPose in a self-supervision framework. We further build EvRealHands, the first large-scale real-world event-based hand pose dataset on several challenging scenes to bridge the domain gap due to relying on synthetic data and facilitate future research. Experiments on EvRealHands demonstrate that EvHandPose outperforms previous event-based method under all evaluation scenes with 15 $\sim$ 20 mm lower MPJPE and achieves accurate and stable hand pose estimation in fast motion and strong light scenes compared with RGB-based methods. Furthermore, EvHandPose demonstrates 3D hand pose estimation at 120 fps or higher.

本文提出了一种基于Event camera的EvHandPose算法，通过Event-to-Pose模块中的新型手势流表示法，以及Pose-to-IWE模块中的对比度最大化和边缘限制来解决手部运动信息编码和运动模糊问题，在自我监督框架下设计EvHandPose，同时还建立了第一个大规模真实世界基础的事件驱动手部姿态数据集，实现了在快速运动和强光等场景下的稳定且精确的手部姿态估计，并实现了120 fps或更高的3D手部姿态估计。

EvHandPose: 基于事件的稀疏监督三维手势姿态估计