Iprnn: An Information-Preserving Model For Video Prediction Using Spatiotemporal Grus

Videos are typically encoded to low-dimensional features to save computation resources for video prediction models. However, the unacceptable information loss while encoding is restricting the performance of the predictive models. To solve this problem, in this paper, we propose an Information- Preserving Spatiotemporal Predictive Model for video prediction, denoted as IPRNN. In our method, we apply multiple skip-connections between the corresponding layers between the encoders and decoders. In this way, more useful information from the encoders can be recalled by the decoders to achieve a more satisfactory performance. Moreover, to further save the computation resources for predictive models, we design a spatiotemporal gated recurrent unit (STGRU), which can efficiently capture the spatial appearance information and temporal motion information for videos. Experimental results show that the proposed method can obtain better performance compared with other state-of-the-art methods.