  3D hand-mesh reconstruction from RGB images facilitates many applications,
including augmented reality (AR). However, this requires not only real-time
speed and accurate hand pose and shape but also plausible mesh-image alignment.
While existing works already achieve promising results, meeting all three
requirements is very challenging. This paper presents a novel pipeline by
decoupling the hand-mesh reconstruction task into three stages: a joint stage
to predict hand joints and segmentation; a mesh stage to predict a rough hand
mesh; and a refine stage to fine-tune it with an offset mesh for mesh-image
alignment. With careful design in the network structure and in the loss
functions, we can promote high-quality finger-level mesh-image alignment and
drive the models together to deliver real-time predictions. Extensive
quantitative and qualitative results on benchmark datasets demonstrate that the
quality of our results outperforms the state-of-the-art methods on
hand-mesh/pose precision and hand-image alignment. In the end, we also showcase
several real-time AR scenarios.
