  Robotic grasping of house-hold objects has made remarkable progress in recent
years. Yet, human grasps are still difficult to synthesize realistically. There
are several key reasons: (1) the human hand has many degrees of freedom (more
than robotic manipulators); (2) the synthesized hand should conform to the
surface of the object; and (3) it should interact with the object in a
semantically and physically plausible manner. To make progress in this
direction, we draw inspiration from the recent progress on learning-based
implicit representations for 3D object reconstruction. Specifically, we propose
an expressive representation for human grasp modelling that is efficient and
easy to integrate with deep neural networks. Our insight is that every point in
a three-dimensional space can be characterized by the signed distances to the
surface of the hand and the object, respectively. Consequently, the hand, the
object, and the contact area can be represented by implicit surfaces in a
common space, in which the proximity between the hand and the object can be
modelled explicitly. We name this 3D to 2D mapping as Grasping Field,
parameterize it with a deep neural network, and learn it from data. We
demonstrate that the proposed grasping field is an effective and expressive
representation for human grasp generation. Specifically, our generative model
is able to synthesize high-quality human grasps, given only on a 3D object
point cloud. The extensive experiments demonstrate that our generative model
compares favorably with a strong baseline and approaches the level of natural
human grasps. Our method improves the physical plausibility of the hand-object
contact reconstruction and achieves comparable performance for 3D hand
reconstruction compared to state-of-the-art methods.
