# Long-Term Video Interpolation with Bidirectional Predictive Network

arXiv: https://arxiv.org/abs/1706.03947 · HTML: https://mlsanity.com/paper/1706.03947/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2017.

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

- [Toward Stable World Models: Measuring and Addressing World Instability in Generative Environments](https://mlsanity.com/paper/2503.08122/) (2025)
- [AdaFNIO: Adaptive Fourier Neural Interpolation Operator for video frame interpolation](https://mlsanity.com/paper/2211.10791/) (2022)
- [Time-adaptive Video Frame Interpolation based on Residual Diffusion](https://mlsanity.com/paper/2504.05402/) (2025)
- [Physical Context and Timing Aware Sequence Generating GANs](https://mlsanity.com/paper/2110.04077/) (2021)

## Abstract (arXiv)

This paper considers the challenging task of long-term video interpolation. Unlike most existing methods that only generate few intermediate frames between existing adjacent ones, we attempt to speculate or imagine the procedure of an episode and further generate multiple frames between two non-consecutive frames in videos. In this paper, we present a novel deep architecture called bidirectional predictive network (BiPN) that predicts intermediate frames from two opposite directions. The bidirectional architecture allows the model to learn scene transformation with time as well as generate longer video sequences. Besides, our model can be extended to predict multiple possible procedures by sampling different noise vectors. A joint loss composed of clues in image and feature spaces and adversarial loss is designed to train our model. We demonstrate the advantages of BiPN on two benchmarks Moving 2D Shapes and UCF101 and report competitive results to recent approaches.