  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/.
