  While predicting robot grasps with parallel jaw grippers have been well
studied and widely applied in robot manipulation tasks, the study on natural
human grasp generation with a multi-finger hand remains a very challenging
problem. In this paper, we propose to generate human grasps given a 3D object
in the world. Our key observation is that it is crucial to model the
consistency between the hand contact points and object contact regions. That
is, we encourage the prior hand contact points to be close to the object
surface and the object common contact regions to be touched by the hand at the
same time. Based on the hand-object contact consistency, we design novel
objectives in training the human grasp generation model and also a new
self-supervised task which allows the grasp generation network to be adjusted
even during test time. Our experiments show significant improvement in human
grasp generation over state-of-the-art approaches by a large margin. More
interestingly, by optimizing the model during test time with the
self-supervised task, it helps achieve larger gain on unseen and out-of-domain
objects. Project page: https://hwjiang1510.github.io/GraspTTA/
