MagicHOI integrates a novel view synthesis diffusion prior with a visibility-aware weighting strategy to reconstruct accurate hand-object 3D shapes from short monocular videos with partial object visibility.
A Dataset of Relighted 3D Interacting Hands
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abstract
The two-hand interaction is one of the most challenging signals to analyze due to the self-similarity, complicated articulations, and occlusions of hands. Although several datasets have been proposed for the two-hand interaction analysis, all of them do not achieve 1) diverse and realistic image appearances and 2) diverse and large-scale groundtruth (GT) 3D poses at the same time. In this work, we propose Re:InterHand, a dataset of relighted 3D interacting hands that achieve the two goals. To this end, we employ a state-of-the-art hand relighting network with our accurately tracked two-hand 3D poses. We compare our Re:InterHand with existing 3D interacting hands datasets and show the benefit of it. Our Re:InterHand is available in https://mks0601.github.io/ReInterHand/.
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MagicHOI: Leveraging 3D Priors for Accurate Hand-object Reconstruction from Short Monocular Video Clips
MagicHOI integrates a novel view synthesis diffusion prior with a visibility-aware weighting strategy to reconstruct accurate hand-object 3D shapes from short monocular videos with partial object visibility.