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Despite video forecasting has been a widely explored topic in recent years,the mainstream of the existing work still limits their models with a singleprediction space but completely neglects the way to leverage their model withmulti-prediction spaces. This work fills this gap. For the first time, wedeeply study numerous strategies to perform video forecasting inmulti-prediction spaces and fuse their results together to boost performance.The prediction in the pixel space usually lacks the ability to preserve thesemantic and structure content of the video however the prediction in thehigh-level feature space is prone to generate errors in the reduction andrecovering process. Therefore, we build a recurrent connection betweendifferent feature spaces and incorporate their generations in the upsamplingprocess. Rather surprisingly, this simple idea yields a much more significantperformance boost than PhyDNet (performance improved by 32.1% MAE on MNIST-2dataset, and 21.4% MAE on KTH dataset). Both qualitative and quantitativeevaluations on four datasets demonstrate the generalization ability andeffectiveness of our approach. We show that our model significantly reduces thetroublesome distortions and blurry artifacts and brings remarkable improvementsto the accuracy in long term video prediction. The code will be released soon.

From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting