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Authors: Muhammad Asad Ali 1 ; 2 ; Nadia Robertini 1 and Didier Stricker 1 ; 2

Affiliations: 1 Augmented Vision Group, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany ; 2 Department of Computer Science, University of Kaiserslautern-Landau (RPTU), Kaiserslautern, Germany

Keyword(s): Hand Reconstruction, Hand Pose Estimation, Multi-View Reconstruction.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Ali, M. A., Robertini, N. and Stricker, D. (2025). HandMvNet: Real-Time 3D Hand Pose Estimation Using Multi-View Cross-Attention Fusion. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3; ISSN 2184-4321, SciTePress, pages 555-562. DOI: 10.5220/0013107300003912

@conference{visapp25,
author={Muhammad Asad Ali and Nadia Robertini and Didier Stricker},
title={HandMvNet: Real-Time 3D Hand Pose Estimation Using Multi-View Cross-Attention Fusion},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={555-562},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013107300003912},
isbn={978-989-758-728-3},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - HandMvNet: Real-Time 3D Hand Pose Estimation Using Multi-View Cross-Attention Fusion
SN - 978-989-758-728-3
IS - 2184-4321
AU - Ali, M.
AU - Robertini, N.
AU - Stricker, D.
PY - 2025
SP - 555
EP - 562
DO - 10.5220/0013107300003912
PB - SciTePress

- Science and Technology Publications, Lda.
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