# Future Frame Prediction of a Video Sequence

arXiv: https://arxiv.org/abs/2009.01689 · HTML: https://mlsanity.com/paper/2009.01689/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [MetaDance: Few-shot Dancing Video Retargeting via Temporal-aware Meta-learning](https://mlsanity.com/paper/2201.04851/) (2022)
- [Enabling Real-time Neural Recovery for Cloud Gaming on Mobile Devices](https://mlsanity.com/paper/2307.07847/) (2023)

## Abstract (arXiv)

Predicting future frames of a video sequence has been a problem of high interest in the field of Computer Vision as it caters to a multitude of applications. The ability to predict, anticipate and reason about future events is the essence of intelligence and one of the main goals of decision-making systems such as human-machine interaction, robot navigation and autonomous driving. However, the challenge lies in the ambiguous nature of the problem as there may be multiple future sequences possible for the same input video shot. A naively designed model averages multiple possible futures into a single blurry prediction. Recently, two distinct approaches have attempted to address this problem as: (a) use of latent variable models that represent underlying stochasticity and (b) adversarially trained models that aim to produce sharper images. A latent variable model often struggles to produce realistic results, while an adversarially trained model underutilizes latent variables and thus fails to produce diverse predictions. These methods have revealed complementary strengths and weaknesses. Combining the two approaches produces predictions that appear more realistic and better cover the range of plausible futures. This forms the basis and objective of study in this project work. In this paper, we proposed a novel multi-scale architecture combining both approaches. We validate our proposed model through a series of experiments and empirical evaluations on Moving MNIST, UCF101, and Penn Action datasets. Our method outperforms the results obtained using the baseline methods.