MLPHand: Real Time Multi-View 3D Hand Mesh Reconstruction via MLP Modeling
This work addresses the computational bottleneck in hand reconstruction for virtual reality and human-computer interaction applications, offering an incremental improvement.
The paper tackled the challenge of real-time multi-view 3D hand mesh reconstruction by proposing MLPHand, which reduces computational complexity by 90% while maintaining comparable accuracy to state-of-the-art methods.
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.