Title: Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry,
Abstract: We propose a deformable generator model to disentangle the appearance and
geometric information from images into two independent latent vectors. The
appearance generator produces the appearance information, including color,
illumination, identity or category, of an image. The geometric generator
produces displacement of the coordinates of each pixel and performs geometric
warping, such as stretching and rotation, on the appearance generator to obtain
the final synthesized image. The proposed model can learn both representations
from image data in an unsupervised manner. The learned geometric generator can
be conveniently transferred to the other image datasets to facilitate
downstream AI tasks.