  Real-time tracking of 3D hand pose in world space is a challenging problem
and plays an important role in VR interaction. Existing work in this space are
limited to either producing root-relative (versus world space) 3D pose or rely
on multiple stages such as generating heatmaps and kinematic optimization to
obtain 3D pose. Moreover, the typical VR scenario, which involves multi-view
tracking from wide \ac{fov} cameras is seldom addressed by these methods. In
this paper, we present a unified end-to-end differentiable framework for
multi-view, multi-frame hand tracking that directly predicts 3D hand pose in
world space. We demonstrate the benefits of end-to-end differentiabilty by
extending our framework with downstream tasks such as jitter reduction and
pinch prediction. To demonstrate the efficacy of our model, we further present
a new large-scale egocentric hand pose dataset that consists of both real and
synthetic data. Experiments show that our system trained on this dataset
handles various challenging interactive motions, and has been successfully
applied to real-time VR applications.
