  This work presents an analysis of the discriminators used in Generative
Adversarial Networks (GANs) for Video. We show that unconstrained video
discriminator architectures induce a loss surface with high curvature which
make optimisation difficult. We also show that this curvature becomes more
extreme as the maximal kernel dimension of video discriminators increases. With
these observations in hand, we propose a family of efficient Lower-Dimensional
Video Discriminators for GANs (LDVD GANs). The proposed family of
discriminators improve the performance of video GAN models they are applied to
and demonstrate good performance on complex and diverse datasets such as
UCF-101. In particular, we show that they can double the performance of
Temporal-GANs and provide for state-of-the-art performance on a single GPU.
