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、人机交接实现以及从二维和三维人际交互数据中估计交接参数的人工智能（AI）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和三维分割结果、给予者与接收者的舒适度评分，以及配对的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组包含角色互换，全部由40名参与者组织完成。我们还展示了使用HOH训练的神经网络在抓取、朝向和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代表了自然的人-人交接交互，克服了带标记数据集需要专门穿戴设备进行身体追踪且缺乏高分辨率手部追踪的挑战。截至目前，HOH是物体数量、参与者人数、包含角色互换的配对组数和捕捉交互总数方面最大的交接数据集。