  Reconstructing the hand mesh from one single RGB image is a challenging task
because hands are often occluded by other objects. Most previous works attempt
to explore more additional information and adopt attention mechanisms for
improving 3D reconstruction performance, while it would increase computational
complexity simultaneously. To achieve a performance-reserving architecture with
high computational efficiency, in this work, we propose a simple but effective
3D hand mesh reconstruction network (i.e., HandS3C), which is the first time to
incorporate state space model into the task of hand mesh reconstruction. In the
network, we design a novel state-space spatial-channel attention module that
extends the effective receptive field, extracts hand features in the spatial
dimension, and enhances regional features of hands in the channel dimension.
This helps to reconstruct a complete and detailed hand mesh. Extensive
experiments conducted on well-known datasets facing heavy occlusions (such as
FREIHAND, DEXYCB, and HO3D) demonstrate that our proposed HandS3C achieves
state-of-the-art performance while maintaining a minimal parameters.
