  Physical contact between hands and objects plays a critical role in human
grasps. We show that optimizing the pose of a hand to achieve expected contact
with an object can improve hand poses inferred via image-based methods. Given a
hand mesh and an object mesh, a deep model trained on ground truth contact data
infers desirable contact across the surfaces of the meshes. Then, ContactOpt
efficiently optimizes the pose of the hand to achieve desirable contact using a
differentiable contact model. Notably, our contact model encourages mesh
interpenetration to approximate deformable soft tissue in the hand. In our
evaluations, our methods result in grasps that better match ground truth
contact, have lower kinematic error, and are significantly preferred by human
participants. Code and models are available online.
