HandS3C: 3D Hand Mesh Reconstruction with State Space Spatial Channel Attention from RGB images

TL;DR

This paper introduces HandS3C, a novel 3D hand mesh reconstruction network that incorporates a state space spatial-channel attention module, achieving high accuracy with minimal parameters on occluded hand images.

Contribution

It is the first to integrate a state space model into 3D hand mesh reconstruction, improving feature extraction and detail recovery while maintaining computational efficiency.

Findings

Achieves state-of-the-art results on multiple datasets.

Handles heavy occlusions effectively.

Maintains minimal model parameters.

Abstract

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.