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This paper proposes a novel model for video generation and especially makesthe attempt to deal with the problem of video generation from textdescriptions, i.e., synthesizing realistic videos conditioned on given texts.Existing video generation methods cannot be easily adapted to handle this taskwell, due to the frame discontinuity issue and their text-free generationschemes. To address these problems, we propose a recurrent deconvolutionalgenerative adversarial network (RD-GAN), which includes a recurrentdeconvolutional network (RDN) as the generator and a 3D convolutional neuralnetwork (3D-CNN) as the discriminator. The RDN is a deconvolutional version ofconventional recurrent neural network, which can well model the long-rangetemporal dependency of generated video frames and make good use of conditionalinformation. The proposed model can be jointly trained by pushing the RDN togenerate realistic videos so that the 3D-CNN cannot distinguish them from realones. We apply the proposed RD-GAN to a series of tasks including conventionalvideo generation, conditional video generation, video prediction and videoclassification, and demonstrate its effectiveness by achieving wellperformance.

Recurrent Deconvolutional Generative Adversarial Networks with Application to Text Guided Video Generation