Dynamic Hyperbolic Attention Network for Fine Hand-object Reconstruction

Zhiying Leng, Shun Cheng Wu, Mahdi Saleh, Antonio Montanaro, Hao Yu, Yin Wang, Nassir Navab, Xiaohui Liang, Federico Tombari

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

2 Zitate (Scopus)

Abstract

Reconstructing both objects and hands in 3D from a single RGB image is complex. Existing methods rely on manually defined hand-object constraints in Euclidean space, leading to suboptimal feature learning. Compared with Euclidean space, hyperbolic space better preserves the geometric properties of meshes thanks to its exponentially-growing space distance, which amplifies the differences between the features based on similarity. In this work, we propose the first precise hand-object reconstruction method in hyperbolic space, namely Dynamic Hyperbolic Attention Network (DHANet), which leverages intrinsic properties of hyperbolic space to learn representative features. Our method that projects mesh and image features into a unified hyperbolic space includes two modules, i.e. dynamic hyperbolic graph convolution and image-attention hyperbolic graph convolution. With these two modules, our method learns mesh features with rich geometry-image multi-modal information and models better hand-object interaction. Our method provides a promising alternative for fine hand-object reconstruction in hyperbolic space. Extensive experiments on three public datasets demonstrate that our method outperforms most state-of-the-art methods.

OriginalspracheEnglisch
TitelProceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten14848-14858
Seitenumfang11
ISBN (elektronisch)9798350307184
DOIs
PublikationsstatusVeröffentlicht - 2023
Veranstaltung2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 - Paris, Frankreich
Dauer: 2 Okt. 20236 Okt. 2023

Publikationsreihe

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499

Konferenz

Konferenz2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
Land/GebietFrankreich
OrtParis
Zeitraum2/10/236/10/23

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