  Generating a pose-invariant representation capable of synthesizing multiple
face pose views from a single pose is still a difficult problem. The solution
is demanded in various areas like multimedia security, computer vision,
robotics, etc. Generative adversarial networks (GANs) have encoder-decoder
structures possessing the capability to learn pose-independent representation
incorporated with discriminator network for realistic face synthesis. We
present PIGAN, a cyclic shared encoder-decoder framework, in an attempt to
solve the problem. As compared to traditional GAN, it consists of secondary
encoder-decoder framework sharing weights from the primary structure and
reconstructs the face with the original pose. The primary framework focuses on
creating disentangle representation, and secondary framework aims to restore
the original face. We use CFP high-resolution, realistic dataset to check the
performance.
