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
this https URL

该研究提出了 Re:InterHand 数据集，通过使用先进的手部重光网络和准确跟踪的两只手的三维姿势，实现了多样和逼真的图像外观和多样和大规模的地面真实（GT）三维姿势，在现有的三维互动手数据集的基础上进行了对比，并展示了其益处。