  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 hand-edge constraints in Pose-to-IWE (Image with Warped
Events) module and formulate EvHandPose in a weakly-supervision framework. We
further build EvRealHands, the first large-scale real-world event-based hand
pose dataset on several challenging scenes to bridge the real-synthetic domain
gap. Experiments on EvRealHands demonstrate that EvHandPose outperforms
previous event-based methods under all evaluation scenes, achieves accurate and
stable hand pose estimation with high temporal resolution in fast motion and
strong light scenes compared with RGB-based methods, generalizes well to
outdoor scenes and another type of event camera, and shows the potential for
the hand gesture recognition task.
