  In this work, we propose a framework for single-view hand mesh
reconstruction, which can simultaneously achieve high reconstruction accuracy,
fast inference speed, and temporal coherence. Specifically, for 2D encoding, we
propose lightweight yet effective stacked structures. Regarding 3D decoding, we
provide an efficient graph operator, namely depth-separable spiral convolution.
Moreover, we present a novel feature lifting module for bridging the gap
between 2D and 3D representations. This module begins with a map-based position
regression (MapReg) block to integrate the merits of both heatmap encoding and
position regression paradigms for improved 2D accuracy and temporal coherence.
Furthermore, MapReg is followed by pose pooling and pose-to-vertex lifting
approaches, which transform 2D pose encodings to semantic features of 3D
vertices. Overall, our hand reconstruction framework, called MobRecon,
comprises affordable computational costs and miniature model size, which
reaches a high inference speed of 83FPS on Apple A14 CPU. Extensive experiments
on popular datasets such as FreiHAND, RHD, and HO3Dv2 demonstrate that our
MobRecon achieves superior performance on reconstruction accuracy and temporal
coherence. Our code is publicly available at
https://github.com/SeanChenxy/HandMesh.
