mlsanityData to 14 Oct 2026

HandNeRF: Learning to Reconstruct Hand-Object Interaction Scene from a Single RGB Image

arXiv 2309.07891 ↗First posted 14 Sep 2023cs.CVcited by 10

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Abstract (arXiv)

This paper presents a method to learn hand-object interaction prior for reconstructing a 3D hand-object scene from a single RGB image. The inference as well as training-data generation for 3D hand-object scene reconstruction is challenging due to the depth ambiguity of a single image and occlusions by the hand and object. We turn this challenge into an opportunity by utilizing the hand shape to constrain the possible relative configuration of the hand and object geometry. We design a generalizable implicit function, HandNeRF, that explicitly encodes the correlation of the 3D hand shape features and 2D object features to predict the hand and object scene geometry. With experiments on real-world datasets, we show that HandNeRF is able to reconstruct hand-object scenes of novel grasp configurations more accurately than comparable methods. Moreover, we demonstrate that object reconstruction from HandNeRF ensures more accurate execution of downstream tasks, such as grasping and motion planning for robotic hand-over and manipulation. Homepage: https://samsunglabs.github.io/HandNeRF-project-page/

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 45 of its 54 references that are in the map. First posted on arXiv 14 Sep 2023.

Builds on

45 of 54 references.

References · 45

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33 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 2.

10 papers.