# Frequency Domain Transformer Networks for Video Prediction

arXiv: https://arxiv.org/abs/1903.00271 · HTML: https://mlsanity.com/paper/1903.00271/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2019.

## Built on by (most cited first, 6 of 6)

- [Local Frequency Domain Transformer Networks for Video Prediction](https://mlsanity.com/paper/2105.04637/) (2021)
- [TaylorSwiftNet: Taylor Driven Temporal Modeling for Swift Future Frame Prediction](https://mlsanity.com/paper/2110.14392/) (2021)
- [The Power of Next-Frame Prediction for Learning Physical Laws](https://mlsanity.com/paper/2405.17450/) (2024)
- [Semantic Prediction: Which One Should Come First, Recognition or Prediction?](https://mlsanity.com/paper/2110.02829/) (2021)
- [Fourier-based Video Prediction through Relational Object Motion](https://mlsanity.com/paper/2110.05881/) (2021)
- [VideoPCDNet: Video Parsing and Prediction with Phase Correlation Networks](https://mlsanity.com/paper/2506.19621/) (2025)

## Abstract (arXiv)

The task of video prediction is forecasting the next frames given some previous frames. Despite much recent progress, this task is still challenging mainly due to high nonlinearity in the spatial domain. To address this issue, we propose a novel architecture, Frequency Domain Transformer Network (FDTN), which is an end-to-end learnable model that estimates and uses the transformations of the signal in the frequency domain. Experimental evaluations show that this approach can outperform some widely used video prediction methods like Video Ladder Network (VLN) and Predictive Gated Pyramids (PGP).