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.

研究提出了 MLPHand，一种用于实时多视图单手重建的新方法。MLPHand 包含两个主要模块：轻量级的基于 MLP 的 Skeleton2Mesh 模型，以及使用多视图几何特征融合预测模块增强 Skeleton2Mesh 模型。实验表明，MLPHand 可以在保持与现有最先进方法相当的重建准确性的同时，将计算复杂度减少 90%。