Predicting Future Frames Using Retrospective Cycle GAN

Recent advances in deep learning have significantly improved the performance of video prediction, however, top-performing algorithms start to generate blurry predictions as they attempt to predict farther future frames. In this paper, we propose a unified generative adversarial network for predicting accurate and temporally consistent future frames over time, even in a challenging environment. The key idea is to train a single generator that can predict both future and past frames while enforcing the consistency of bi-directional prediction using the retrospective cycle constraints. Moreover, we employ two discriminators not only to identify fake frames but also to distinguish fake contained image sequences from the real sequence. The latter discriminator, the sequence discriminator, plays a crucial role in predicting temporally consistent future frames. We experimentally verify the proposed framework using various real-world videos captured by car-mounted cameras, surveillance cameras, and arbitrary devices with state-of-the-art methods.