  Recent years have witnessed great success for hand reconstruction in
real-time applications such as visual reality and augmented reality while
interacting with two-hand reconstruction through efficient transformers is left
unexplored. In this paper, we propose a method called lightweight attention
hand (LWA-HAND) to reconstruct hands in low flops from a single RGB image. To
solve the occlusion and interaction problem in efficient attention
architectures, we propose three mobile attention modules in this paper. The
first module is a lightweight feature attention module that extracts both local
occlusion representation and global image patch representation in a
coarse-to-fine manner. The second module is a cross image and graph bridge
module which fuses image context and hand vertex. The third module is a
lightweight cross-attention mechanism that uses element-wise operation for the
cross-attention of two hands in linear complexity. The resulting model achieves
comparable performance on the InterHand2.6M benchmark in comparison with the
state-of-the-art models. Simultaneously, it reduces the flops to $0.47GFlops$
while the state-of-the-art models have heavy computations between $10GFlops$
and $20GFlops$.
