  In this paper, we propose a novel technique for generating images in the 3D
domain from images with high degree of geometrical transformations. By
coalescing two popular concurrent methods that have seen rapid ascension to the
machine learning zeitgeist in recent years: GANs (Goodfellow et. al.) and
Capsule networks (Sabour, Hinton et. al.) - we present: \textbf{CapsGAN}. We
show that CapsGAN performs better than or equal to traditional CNN based GANs
in generating images with high geometric transformations using rotated MNIST.
In the process, we also show the efficacy of using capsules architecture in the
GANs domain. Furthermore, we tackle the Gordian Knot in training GANs - the
performance control and training stability by experimenting with using
Wasserstein distance (gradient clipping, penalty) and Spectral Normalization.
The experimental findings of this paper should propel the application of
capsules and GANs in the still exciting and nascent domain of 3D image
generation, and plausibly video (frame) generation.
