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Video prediction is a pixel-wise dense prediction task to infer future framesbased on past frames. Missing appearance details and motion blur are still twomajor problems for current predictive models, which lead to image distortionand temporal inconsistency. In this paper, we point out the necessity ofexploring multi-frequency analysis to deal with the two problems. Inspired bythe frequency band decomposition characteristic of Human Vision System (HVS),we propose a video prediction network based on multi-level wavelet analysis todeal with spatial and temporal information in a unified manner. Specifically,the multi-level spatial discrete wavelet transform decomposes each video frameinto anisotropic sub-bands with multiple frequencies, helping to enrichstructural information and reserve fine details. On the other hand, multi-leveltemporal discrete wavelet transform which operates on time axis decomposes theframe sequence into sub-band groups of different frequencies to accuratelycapture multi-frequency motions under a fixed frame rate. Extensive experimentson diverse datasets demonstrate that our model shows significant improvementson fidelity and temporal consistency over state-of-the-art works.

Exploring Spatial-Temporal Multi-Frequency Analysis for High-Fidelity and Temporal-Consistency Video Prediction