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arXiv · 2608.20093

HandMvNet: Real-Time 3D Hand Pose Estimation Using Multi-View Cross-Attention Fusion

Muhammad Asad Ali·Nadia Robertini·Didier Stricker
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Abstract

In this work, we present HandMvNet, one of the first real-time method designed to estimate 3D hand motion and shape from multi-view camera images. Unlike previous monocular approaches, which suffer from scale-depth ambiguities, our method ensures consistent and accurate absolute hand poses and shapes. This is achieved through a multi-view attention-fusion mechanism that effectively integrates features from multiple viewpoints. In contrast to previous multi-view methods, our approach eliminates the need for camera parameters as input to learn 3D geometry. HandMvNet also achieves a substantial reduction in inference time while delivering competitive results compared to the state-of-the-art methods, making it suitable for real-time applications. Evaluated on publicly available datasets, HandMvNet qualitatively and quantitatively outperforms previous methods under identical settings. Code is available at github.com/pyxploiter/handmvnet.

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Muhammad Asad Ali, Nadia Robertini, Didier Stricker. 2026-08-20. HandMvNet: Real-Time 3D Hand Pose Estimation Using Multi-View Cross-Attention Fusion. https://doi.org/10.5220/0013107300003912

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