Recent breakthroughs in adversarial generative modeling have led to models
capable of producing video samples of high quality, even on large and complex
datasets of real-world video. In this work, we focus on the task of video
prediction, where given a sequence of frames extracted from a video, the goal
is to generate a plausible future sequence. We first improve the state of the
art by performing a systematic empirical study of discriminator decompositions
and proposing an architecture that yields faster convergence and higher
performance than previous approaches. We then analyze recurrent units in the
generator, and propose a novel recurrent unit which transforms its past hidden
state according to predicted motion-like features, and refines it to handle
dis-occlusions, scene changes and other complex behavior. We show that this
recurrent unit consistently outperforms previous designs. Our final model leads
to a leap in the state-of-the-art performance, obtaining a test set Frechet
Video Distance of 25.7, down from 69.2, on the large-scale Kinetics-600
dataset.

本文通过对辩别器分解的系统实证研究，提出了一种结构，使得其收敛速度和性能都优于之前的方法，并分析了生成器中的循环单元，提出了一种新的循环单元，可以根据预测的动态特征转换其过去的隐藏状态，包括处理去除、场景改变等复杂行为，最终的模型在大规模的 Kinetics-600 数据集上，获得了卓越的性能。