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Although many video prediction methods have obtained good performance inlow-resolution (64$\sim$128) videos, predictive models for high-resolution(512$\sim$4K) videos have not been fully explored yet, which are moremeaningful due to the increasing demand for high-quality videos. Compared withlow-resolution videos, high-resolution videos contain richer appearance(spatial) information and more complex motion (temporal) information. In thispaper, we propose a Spatiotemporal Residual Predictive Model (STRPM) forhigh-resolution video prediction. On the one hand, we propose a SpatiotemporalEncoding-Decoding Scheme to preserve more spatiotemporal information forhigh-resolution videos. In this way, the appearance details for each frame canbe greatly preserved. On the other hand, we design a Residual Predictive Memory(RPM) which focuses on modeling the spatiotemporal residual features (STRF)between previous and future frames instead of the whole frame, which cangreatly help capture the complex motion information in high-resolution videos.In addition, the proposed RPM can supervise the spatial encoder and temporalencoder to extract different features in the spatial domain and the temporaldomain, respectively. Moreover, the proposed model is trained using generativeadversarial networks (GANs) with a learned perceptual loss (LP-loss) to improvethe perceptual quality of the predictions. Experimental results show that STRPMcan generate more satisfactory results compared with various existing methods.

STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction