  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.
