# DFPN: Deformable Frame Prediction Network

arXiv: https://arxiv.org/abs/2105.12794 · HTML: https://mlsanity.com/paper/2105.12794/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [TDAN: Temporally Deformable Alignment Network for Video Super-Resolution](https://mlsanity.com/paper/1812.02898/) (2018)
- [A Review on Deep Learning Techniques for Video Prediction](https://mlsanity.com/paper/2004.05214/) (2020)
- [Learning to Generate Long-term Future via Hierarchical Prediction](https://mlsanity.com/paper/1704.05831/) (2017)
- [SDCNet: Video Prediction Using Spatially-Displaced Convolution](https://mlsanity.com/paper/1811.00684/) (2018)
- [GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling](https://mlsanity.com/paper/1905.11172/) (2019)
- [End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression](https://mlsanity.com/paper/2008.05028/) (2020)
- [Can Learned Frame-Prediction Compete with Block-Motion Compensation for Video Coding?](https://mlsanity.com/paper/2007.08922/) (2020)
- [Effect of Architectures and Training Methods on the Performance of Learned Video Frame Prediction](https://mlsanity.com/paper/2008.06106/) (2020)

## Built on by (most cited first, 4 of 4)

- [The Power of Next-Frame Prediction for Learning Physical Laws](https://mlsanity.com/paper/2405.17450/) (2024)
- [Everything is a Video: Unifying Modalities through Next-Frame Prediction](https://mlsanity.com/paper/2411.10503/) (2024)
- [Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention](https://mlsanity.com/paper/2209.10192/) (2022)
- [FG-DFPN: Flow Guided Deformable Frame Prediction Network](https://mlsanity.com/paper/2503.11343/) (2025)

## Abstract (arXiv)

Learned frame prediction is a current problem of interest in computer vision and video compression. Although several deep network architectures have been proposed for learned frame prediction, to the best of our knowledge, there is no work based on using deformable convolutions for frame prediction. To this effect, we propose a deformable frame prediction network (DFPN) for task oriented implicit motion modeling and next frame prediction. Experimental results demonstrate that the proposed DFPN model achieves state of the art results in next frame prediction. Our models and results are available at https://github.com/makinyilmaz/DFPN.