mlsanityData to 14 Oct 2026

LWA-HAND: Lightweight Attention Hand for Interacting Hand Reconstruction

arXiv 2208.09815 ↗First posted 21 Aug 2022cs.CVcited by 5

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Abstract (arXiv)

Recent years have witnessed great success for hand reconstruction in real-time applications such as visual reality and augmented reality while interacting with two-hand reconstruction through efficient transformers is left unexplored. In this paper, we propose a method called lightweight attention hand (LWA-HAND) to reconstruct hands in low flops from a single RGB image. To solve the occlusion and interaction problem in efficient attention architectures, we propose three mobile attention modules in this paper. The first module is a lightweight feature attention module that extracts both local occlusion representation and global image patch representation in a coarse-to-fine manner. The second module is a cross image and graph bridge module which fuses image context and hand vertex. The third module is a lightweight cross-attention mechanism that uses element-wise operation for the cross-attention of two hands in linear complexity. The resulting model achieves comparable performance on the InterHand2.6M benchmark in comparison with the state-of-the-art models. Simultaneously, it reduces the flops to 0.47GFlops while the state-of-the-art models have heavy computations between 10GFlops and 20GFlops.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 44 of its 55 references that are in the map. First posted on arXiv 21 Aug 2022.

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44 of 55 references.

References · 44

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32 more
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Citing papers per year: 2023: 4; 2024: 0; 2025: 1.

5 papers.