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HOH（人-物-人）交接数据集，这是一个包含136个物体的大规模物体数量数据集，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体交互二维与三维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、抓取类型与手性标签、物体、给予者手部与接收者手部的二维与三维分割、给予者与接收者的舒适度评分、配对物体元数据及对齐的三维模型，涵盖136个物体与20组给予者-接收者配对（其中40组含角色互换）的2,720次交接交互，所有数据由40名参与者采集。我们还展示了基于HOH训练的神经网络在抓取、朝向和轨迹预测中的实验效果。作为唯一完全无标记的交接捕捉数据集，HOH能够呈现自然的人-人交接交互，解决了有标记数据集因需特定身体追踪装置而缺乏高分辨率手部追踪的难题。截至目前，HOH在物体数量、参与者数量、含角色互换的配对数量及总捕捉交互次数上均为最大的交接数据集。