  We present HOReeNet, which tackles the novel task of manipulating images
involving hands, objects, and their interactions. Especially, we are interested
in transferring objects of source images to target images and manipulating 3D
hand postures to tightly grasp the transferred objects. Furthermore, the
manipulation needs to be reflected in the 2D image space. In our reenactment
scenario involving hand-object interactions, 3D reconstruction becomes
essential as 3D contact reasoning between hands and objects is required to
achieve a tight grasp. At the same time, to obtain high-quality 2D images from
3D space, well-designed 3D-to-2D projection and image refinement are required.
Our HOReeNet is the first fully differentiable framework proposed for such a
task. On hand-object interaction datasets, we compared our HOReeNet to the
conventional image translation algorithms and reenactment algorithm. We
demonstrated that our approach could achieved the state-of-the-art on the
proposed task.
