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Generating videos predicting the future of a given sequence has been an areaof active research in recent years. However, an essential problem remainsunsolved: most of the methods require large computational cost and memory usagefor training. In this paper, we propose a novel method for generating futureprediction videos with less memory usage than the conventional methods. This isa critical stepping stone in the path towards generating videos with high imagequality, similar to that of generated images in the latest works in the fieldof image generation. We achieve high-efficiency by training our method in twostages: (1) image reconstruction to encode video frames into latent variables,and (2) latent variable prediction to generate the future sequence. Our methodachieves an efficient compression of video into low-dimensional latentvariables by decomposing each frame according to its hierarchical structure.That is, we consider that video can be separated into background and foregroundobjects, and that each object holds time-varying and time-independentinformation independently. Our experiments show that the proposed method canefficiently generate future prediction videos, even for complex datasets thatcannot be handled by previous methods.

Efficient training for future video generation based on hierarchical disentangled representation of latent variables