HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video
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Author / Producer
Date
2024
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Since humans interact with diverse objects every day, the holistic 3D capture of these interactions is important to understand and model human behaviour. However, most existing methods for hand-object reconstruction from RGB either assume pre-scanned object templates or heavily rely on limited 3D hand-object data, restricting their ability to scale and generalize to more unconstrained interaction settings. To address this, we introduce HOLD - the first category-agnostic method that reconstructs an articulated hand and an object jointly from a monocular interaction video. We develop a compositional articulated implicit model that can reconstruct disentangled 3D hands and objects from 2D images. We also further incorporate hand-object constraints to improve hand-object poses and consequently the reconstruction quality. Our method does not rely on any 3D hand-object annotations while significantly outperforming fully-supervised baselines in both in-the-lab and challenging in-the-wild settings. Moreover, we qualitatively show its robustness in reconstructing from in-the-wild videos. See here for code, data, models, and updates.
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Publication status
published
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Editor
Book title
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Journal / series
Volume
Pages / Article No.
494 - 504
Publisher
IEEE
Event
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)
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Methods
Software
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Date collected
Date created
Subject
Hand-object reconstruction; Hand-object interaction; Human-object interaction; Hand pose estimation
Organisational unit
03979 - Hilliges, Otmar (ehemalig) / Hilliges, Otmar (former)