Papers › VM-BHINet:Vision Mamba Bimanual Hand Interaction Network for 3D Interacting Hand Mesh...
VM-BHINet:Vision Mamba Bimanual Hand Interaction Network for 3D Interacting Hand Mesh Recovery From a Single RGB Image
Han Bi, Ge Yu, Yu He, Wenzhuo LIU, Zijie Zheng
Understanding bimanual hand interactions is essential for realistic 3D pose and shape reconstruction. However, existing methods struggle with occlusions, ambiguous appearances, and computational inefficiencies. To address these challenges, we propose Vision Mamba Bimanual Hand Interaction Network (VM-BHINet), introducing state space models (SSMs) into hand reconstruction to enhance interaction modeling while improving computational efficiency. The core component, Vision Mamba Interaction Feature Extraction Block (VM-IFEBlock), combines SSMs with local and global feature operations, enabling deep understanding of hand interactions. Experiments on the InterHand2.6M dataset show that VM-BHINet reduces Mean per-joint position error (MPJPE) and Mean per-vertex position error (MPVPE) by 2-3%, significantly surpassing state-of-the-art methods.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Interacting Hand Pose Estimation | InterHand2.6M | VM-BHINet | MPJPE Test | 5.09 | #1 of 9 | Archive leaderboard | report |
| 3D Interacting Hand Pose Estimation | InterHand2.6M | VM-BHINet | MPVPE Test | 5.44 | #1 of 9 | Archive leaderboard | report |
| 3D Interacting Hand Pose Estimation | InterHand2.6M | VM-BHINet | MRRPE Test | - | #1 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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