  This paper proposes a novel model for video generation and especially makes
the attempt to deal with the problem of video generation from text
descriptions, i.e., synthesizing realistic videos conditioned on given texts.
Existing video generation methods cannot be easily adapted to handle this task
well, due to the frame discontinuity issue and their text-free generation
schemes. To address these problems, we propose a recurrent deconvolutional
generative adversarial network (RD-GAN), which includes a recurrent
deconvolutional network (RDN) as the generator and a 3D convolutional neural
network (3D-CNN) as the discriminator. The RDN is a deconvolutional version of
conventional recurrent neural network, which can well model the long-range
temporal dependency of generated video frames and make good use of conditional
information. The proposed model can be jointly trained by pushing the RDN to
generate realistic videos so that the 3D-CNN cannot distinguish them from real
ones. We apply the proposed RD-GAN to a series of tasks including conventional
video generation, conditional video generation, video prediction and video
classification, and demonstrate its effectiveness by achieving well
performance.
