  Existing augmented reality (AR) applications often ignore occlusion between
real hands and virtual objects when incorporating virtual objects in our views.
The challenges come from the lack of accurate depth and mismatch between real
and virtual depth. This paper presents GrabAR, a new approach that directly
predicts the real-and-virtual occlusion, and bypasses the depth acquisition and
inference. Our goal is to enhance AR applications with interactions between
hand (real) and grabbable objects (virtual). With paired images of hand and
object as inputs, we formulate a neural network that learns to generate the
occlusion mask. To train the network, we compile a synthetic dataset to
pre-train it and a real dataset to fine-tune it, thus reducing the burden of
manual labels and addressing the domain difference. Then, we embed the trained
network in a prototyping AR system that supports hand grabbing of various
virtual objects, demonstrate the system performance, both quantitatively and
qualitatively, and showcase interaction scenarios, in which we can use bare
hand to grab virtual objects and directly manipulate them.
