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In this paper, we propose a generative model, Temporal Generative AdversarialNets (TGAN), which can learn a semantic representation of unlabeled videos, andis capable of generating videos. Unlike existing Generative Adversarial Nets(GAN)-based methods that generate videos with a single generator consisting of3D deconvolutional layers, our model exploits two different types ofgenerators: a temporal generator and an image generator. The temporal generatortakes a single latent variable as input and outputs a set of latent variables,each of which corresponds to an image frame in a video. The image generatortransforms a set of such latent variables into a video. To deal withinstability in training of GAN with such advanced networks, we adopt a recentlyproposed model, Wasserstein GAN, and propose a novel method to train it stablyin an end-to-end manner. The experimental results demonstrate the effectivenessof our methods.

Temporal Generative Adversarial Nets with Singular Value Clipping