  Given a single image of a general object such as a chair, could we also
restore its articulated 3D shape similar to human modeling, so as to animate
its plausible articulations and diverse motions? This is an interesting new
question that may have numerous downstream augmented reality and virtual
reality applications. Comparing with previous efforts on object manipulation,
our work goes beyond 2D manipulation and rigid deformation, and involves
articulated manipulation. To achieve this goal, we propose an automated
approach to build such 3D generic objects from single images and embed
articulated skeletons in them. Specifically, our framework starts by
reconstructing the 3D object from an input image. Afterwards, to extract
skeletons for generic 3D objects, we develop a novel skeleton prediction method
with a multi-head structure for skeleton probability field estimation by
utilizing the deep implicit functions. A dataset of generic 3D objects with
ground-truth annotated skeletons is collected. Empirically our approach is
demonstrated with satisfactory performance on public datasets as well as our
in-house dataset; our results surpass those of the state-of-the-arts by a
noticeable margin on both 3D reconstruction and skeleton prediction.
