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（人-物-人）交接数据集，这是一个包含136个物体的大数量数据集，旨在加速基于数据驱动的交接研究、人-机器人交接实现，以及基于二维和三维人员交互数据的人工智能交接参数估计研究。HOH包含多视角RGB与深度数据、骨架、融合点云、抓取类型与利手标签、物体、给予手和接收手的二维及三维分割、给予者和接收者的舒适度评分，以及配对物体元数据和对齐的三维模型，共涵盖2,720次交接交互，涉及136个物体和20对给予者-接收者组合（其中40次为角色互换），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、姿态和轨迹预测方面的实验成果。作为唯一完全无标记的交接捕获数据集，HOH能够捕捉自然的人-人交接交互，克服了标记数据集需要身体追踪专用装备且缺乏高分辨率手部追踪的挑战。迄今为止，HOH在物体数量、参与者数量、考虑角色互换的配对对数以及捕获的总交互次数方面，均为最大的交接数据集。