LiteSpiralGCN: Lightweight 3D hand mesh reconstruction via spiral graph convolution

Hand mesh reconstruction technologies play an important role in computer vision, as they facilitate many applications including virtual/augmented reality, human-computer interaction, etc. However, current methods typically rely on computationally intensive architectures with excessive parameters and storage demands to achieve accuracy. In this paper, we propose a lightweight network via Spiral GCN balancing accuracy and efficiency, named LiteSpiralGCN. Our approach includes an Attention Sampling (AS) module to enhance keypoint feature interactions, a SpiralGCN module for efficient and flexible decoding, and a refinement method that leverages multi-scale and multi-stage information to boost reconstruction accuracy. Experiments conducted on benchmark datasets demonstrate that LiteSpiralGCN effectively balances parameter scale and reconstruction accuracy. Specifically, on the FreiHAND dataset, LiteSpiralGCN achieves a PA-MPJPE of 6.5 mm and a PA-MPVPE of 6.6 mm using only 9.77M parameters. Our code is publicly available at: https://github.com/minqili/LiteSpiralGCN.