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Visual signals in a video can be divided into content and motion. Whilecontent specifies which objects are in the video, motion describes theirdynamics. Based on this prior, we propose the Motion and Content decomposedGenerative Adversarial Network (MoCoGAN) framework for video generation. Theproposed framework generates a video by mapping a sequence of random vectors toa sequence of video frames. Each random vector consists of a content part and amotion part. While the content part is kept fixed, the motion part is realizedas a stochastic process. To learn motion and content decomposition in anunsupervised manner, we introduce a novel adversarial learning scheme utilizingboth image and video discriminators. Extensive experimental results on severalchallenging datasets with qualitative and quantitative comparison to thestate-of-the-art approaches, verify effectiveness of the proposed framework. Inaddition, we show that MoCoGAN allows one to generate videos with same contentbut different motion as well as videos with different content and same motion.

MoCoGAN: Decomposing Motion and Content for Video Generation