Transformation-based Adversarial Video Prediction on Large-Scale Data
This work addresses video prediction for applications like robotics and autonomous systems, representing a significant leap in performance but is incremental in method.
The paper tackles video prediction by proposing a novel recurrent unit that transforms past hidden states using predicted motion-like features and refines them for complex behaviors, achieving a test set Frechet Video Distance of 25.7 on Kinetics-600, down from 69.2.
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.