  Multi-view hand mesh reconstruction is a critical task for applications in
virtual reality and human-computer interaction, but it remains a formidable
challenge. Although existing multi-view hand reconstruction methods achieve
remarkable accuracy, they typically come with an intensive computational burden
that hinders real-time inference. To this end, we propose MLPHand, a novel
method designed for real-time multi-view single hand reconstruction. MLP Hand
consists of two primary modules: (1) a lightweight MLP-based Skeleton2Mesh
model that efficiently recovers hand meshes from hand skeletons, and (2) a
multi-view geometry feature fusion prediction module that enhances the
Skeleton2Mesh model with detailed geometric information from multiple views.
Experiments on three widely used datasets demonstrate that MLPHand can reduce
computational complexity by 90% while achieving comparable reconstruction
accuracy to existing state-of-the-art baselines.
