We present the HOH (Human-Object-Human) Handover Dataset, a large object count dataset with 136 objects, to accelerate data-driven research on handover studies, human-robot handover implementation, and artificial intelligence (AI) on handover parameter estimation from 2D and 3D data of person interactions. HOH contains multi-view RGB and depth data, skeletons, fused point clouds, grasp type and handedness labels, object, giver hand, and receiver hand 2D and 3D segmentations, giver and receiver comfort ratings, and paired object metadata and aligned 3D models for 2,720 handover interactions spanning 136 objects and 20 giver-receiver pairs-40 with role-reversal-organized from 40 participants. We also show experimental results of neural networks trained using HOH to perform grasp, orientation, and trajectory prediction. As the only fully markerless handover capture dataset, HOH represents natural human-human handover interactions, overcoming challenges with markered datasets that require specific suiting for body tracking, and lack high-resolution hand tracking. To date, HOH is the largest handover dataset in number of objects, participants, pairs with role reversal accounted for, and total interactions captured.

翻译：我们提出了HOH（Human-Object-Human）交接数据集，该数据集包含136个物体，是大规模物体数量的数据集，旨在加速基于数据驱动的交接研究、人机交接实现，以及基于人员交互2D/3D数据的交接参数估计人工智能研究。HOH包含多视角RGB与深度数据、骨骼数据、融合点云、抓取类型与手性标注、物体、给予手与接收手的2D/3D分割、给予者与接收者的舒适度评分，以及配对物体元数据和对齐3D模型，涵盖136个物体与20对给予-接收者组合（其中40对包含角色互换）的2,720次交接交互，数据来自40名参与者。我们还展示了基于HOH训练的神经网络在抓取、朝向与轨迹预测方面的实验结果。作为唯一完全无标记的交接捕捉数据集，HOH还原了自然的人-人交接交互，克服了标记数据集在人体追踪时需要特定设备适配且缺乏高分辨率手部追踪的挑战。目前，HOH在物体数量、参与者数量、包含角色互换的配对数量以及捕获的总交互次数方面均为最大的交接数据集。