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Authors: Chaitanya Bandi and Ulrike Thomas

Affiliation: Robotics and Human Machine Interaction Lab, Technical University of Chemnitz, Chemnitz, Germany

Keyword(s): Hand, Object, Pose, Reconstruction, Autoencoder.

Abstract: Hands and objects severely occlude each other, making it extremely challenging to estimate the hand-object pose during human-robot interactions. In this work, we propose a framework that jointly estimates 3D hand mesh and 6D object pose in real-time. The framework shares the features of a single network with both the hand pose estimation network and the object pose estimation network. Hand pose estimation is a parametric model that regresses the shape and pose parameters of the hand. The object pose estimation network is a cross-model variational autoencoder network for the direct reconstruction of an object’s 6D pose. Our method shows substantial improvement in object pose estimation on two large-scale open-source datasets.

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Paper citation in several formats:
Bandi, C. and Thomas, U. (2024). Hand Mesh and Object Pose Reconstruction Using Cross Model Autoencoder. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 183-193. DOI: 10.5220/0012370700003660

@conference{visapp24,
author={Chaitanya Bandi. and Ulrike Thomas.},
title={Hand Mesh and Object Pose Reconstruction Using Cross Model Autoencoder},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={183-193},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012370700003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - Hand Mesh and Object Pose Reconstruction Using Cross Model Autoencoder
SN - 978-989-758-679-8
IS - 2184-4321
AU - Bandi, C.
AU - Thomas, U.
PY - 2024
SP - 183
EP - 193
DO - 10.5220/0012370700003660
PB - SciTePress

- Science and Technology Publications, Lda.
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