  Object handover is a common human collaboration behavior that attracts
attention from researchers in Robotics and Cognitive Science. Though visual
perception plays an important role in the object handover task, the whole
handover process has been specifically explored. In this work, we propose a
novel rich-annotated dataset, H2O, for visual analysis of human-human object
handovers. The H2O, which contains 18K video clips involving 15 people who hand
over 30 objects to each other, is a multi-purpose benchmark. It can support
several vision-based tasks, from which, we specifically provide a baseline
method, RGPNet, for a less-explored task named Receiver Grasp Prediction.
Extensive experiments show that the RGPNet can produce plausible grasps based
on the giver's hand-object states in the pre-handover phase. Besides, we also
report the hand and object pose errors with existing baselines and show that
the dataset can serve as the video demonstrations for robot imitation learning
on the handover task. Dataset, model and code will be made public.
