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.

In 3D virtual environments, effective information presentation relies on clear label layouts to annotate complex objects. As label density increases, structured and adaptive layouts become critical to prevent visual clutter and maintain clarity. However, existing label layout methods often overlook the issue of double vision between labels caused by binocular disparity. Additionally, discrete update strategies in 3D label layouts struggle to maintain layout quality during viewpoint changes due to their limited real-time adaptability. To address these issues, this paper presents a binocular 3D dynamic label layout method based on a continuous updating strategy within a force-based framework, generating real-time binocular layouts to minimize visual confusion. Computational evaluation and a user study demonstrate that the proposed method significantly reduces double vision, as quantified by a reduction in double vision degree of up to 15.74%, along with label overlap and leader line crossings. The results further indicate that our approach surpasses existing methods in clarity, aesthetics, adaptability, and user satisfaction, while reducing label reading time and alleviating users’ physical and effort demands, particularly in high-density label environments.