MLPHand: Real Time Multi-view 3D Hand Reconstruction via MLP Modeling
Yang, Jian Li, Jiakun Li, Guoming Wu, Huai-Yu Shen, Zhen Fan, Zhaoxin
Yang, Jian
Li, Jiakun
Li, Guoming
Wu, Huai-Yu
Shen, Zhen
Fan, Zhaoxin
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Multi-view hand 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. MLPHand 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. Project link is https://github.com/jackyyang9/MLPHand.
Citation
J. Yang, J. Li, G. Li, H. Y. Wu, Z. Shen, and Z. Fan, “MLPHand: Real Time Multi-view 3D Hand Reconstruction via MLP Modeling,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 15132, pp. 407–424, Jan. 2025, doi: 10.1007/978-3-031-72904-1_24.
Source
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference
European Conference on Computer Vision (ECCV)
Keywords
Reconstruction, Multi-view Reconstruction, Real-time Inference
Subjects
Source
European Conference on Computer Vision (ECCV)
Publisher
Springer Nature