# Fourier-based Video Prediction through Relational Object Motion

arXiv: https://arxiv.org/abs/2110.05881 · HTML: https://mlsanity.com/paper/2110.05881/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [Deep multi-scale video prediction beyond mean square error](https://mlsanity.com/paper/1511.05440/) (2015)
- [A Review on Deep Learning Techniques for Video Prediction](https://mlsanity.com/paper/2004.05214/) (2020)
- [Local Frequency Domain Transformer Networks for Video Prediction](https://mlsanity.com/paper/2105.04637/) (2021)
- [Frequency Domain Transformer Networks for Video Prediction](https://mlsanity.com/paper/1903.00271/) (2019)

## Built on by (most cited first, 1 of 1)

- [VideoPCDNet: Video Parsing and Prediction with Phase Correlation Networks](https://mlsanity.com/paper/2506.19621/) (2025)

## Abstract (arXiv)

The ability to predict future outcomes conditioned on observed video frames is crucial for intelligent decision-making in autonomous systems. Recently, deep recurrent architectures have been applied to the task of video prediction. However, this often results in blurry predictions and requires tedious training on large datasets. Here, we explore a different approach by (1) using frequency-domain approaches for video prediction and (2) explicitly inferring object-motion relationships in the observed scene. The resulting predictions are consistent with the observed dynamics in a scene and do not suffer from blur.