In this paper we propose a generative model, the Temporal Generative Adversarial Network (TGAN), which can learn a semantic representation of unlabelled videos, and is capable of generating consistent videos. Unlike an existing GAN that generates videos with a generator consisting of 3D deconvolutional layers, our model exploits two types of generators: a temporal generator and an image generator. The temporal generator consists of 1D deconvolutional layers and outputs a set of latent variables, each of which corresponds to a frame in the generated video, and the image generator transforms them into a video with 2D deconvolutional layers. This representation allows efficient training of the network parameters. Moreover, it can handle a wider range of applications including the generation of a long sequence, frame interpolation, and the use of pre-trained models. Experimental results demonstrate the effectiveness of our method.

本文提出了一种生成式模型 Temporal Generative Adversarial Nets（TGAN），用于学习未标记视频的语义表示，并能够生成视频。我们的模型利用两种不同类型的生成器：时间生成器和图像生成器，解决了利用现有的基于GAN的方法生成视频时存在的问题。为了稳定训练，我们采用了最近提出的Wasserstein GAN模型，并提出了一种稳定的端到端训练方法。实验结果表明了我们方法的有效性。

具有奇异值剪切的时间生成对抗网络