  We introduce KeypointDeformer, a novel unsupervised method for shape control
through automatically discovered 3D keypoints. We cast this as the problem of
aligning a source 3D object to a target 3D object from the same object
category. Our method analyzes the difference between the shapes of the two
objects by comparing their latent representations. This latent representation
is in the form of 3D keypoints that are learned in an unsupervised way. The
difference between the 3D keypoints of the source and the target objects then
informs the shape deformation algorithm that deforms the source object into the
target object. The whole model is learned end-to-end and simultaneously
discovers 3D keypoints while learning to use them for deforming object shapes.
Our approach produces intuitive and semantically consistent control of shape
deformations. Moreover, our discovered 3D keypoints are consistent across
object category instances despite large shape variations. As our method is
unsupervised, it can be readily deployed to new object categories without
requiring annotations for 3D keypoints and deformations.
