  We propose a conditional generative adversarial network (GAN) model for
zero-shot video generation. In this study, we have explored zero-shot
conditional generation setting. In other words, we generate unseen videos from
training samples with missing classes. The task is an extension of conditional
data generation. The key idea is to learn disentangled representations in the
latent space of a GAN. To realize this objective, we base our model on the
motion and content decomposed GAN and conditional GAN for image generation. We
build the model to find better-disentangled representations and to generate
good-quality videos. We demonstrate the effectiveness of our proposed model
through experiments on the Weizmann action database and the MUG facial
expression database.
