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/.

翻译：双手交互是最具挑战性的分析信号之一，因其自身相似性、复杂的关节结构以及手部遮挡。尽管已有多个数据集被提出用于双手交互分析，但所有数据集均未能同时实现：1）多样且逼真的图像外观；2）多样且大规模的三维姿态真实标注。本研究提出Re:InterHand，一个重光照的三维交互手部数据集，可同时达成上述两个目标。为此，我们采用最先进的手部重光照网络，并结合精确追踪的双手三维姿态。我们将Re:InterHand与现有三维交互手部数据集进行对比，并展示其优势。我们的Re:InterHand数据集可在https://mks0601.github.io/ReInterHand/获取。