Hierarchical Long-term Video Prediction without Supervision

TL;DR

This paper introduces a hierarchical video prediction model capable of long-term predictions without requiring high-level supervision, utilizing an adversarial loss to enhance prediction quality over approximately 20 seconds.

Contribution

The authors develop a novel training approach that jointly trains encoder, predictor, and decoder without high-level annotations, improving long-term video prediction performance.

Findings

Predicts about 20 seconds into the future.

Achieves better results than previous methods on Human 3.6M dataset.

Operates without high-level supervision.

Abstract

Much of recent research has been devoted to video prediction and generation, yet most of the previous works have demonstrated only limited success in generating videos on short-term horizons. The hierarchical video prediction method by Villegas et al. (2017) is an example of a state-of-the-art method for long-term video prediction, but their method is limited because it requires ground truth annotation of high-level structures (e.g., human joint landmarks) at training time. Our network encodes the input frame, predicts a high-level encoding into the future, and then a decoder with access to the first frame produces the predicted image from the predicted encoding. The decoder also produces a mask that outlines the predicted foreground object (e.g., person) as a by-product. Unlike Villegas et al. (2017), we develop a novel training method that jointly trains the encoder, the predictor, and the decoder together without highlevel supervision; we further improve upon this by using an adversarial loss in the feature space to train the predictor. Our method can predict about 20 seconds into the future and provides better results compared to Denton and Fergus (2018) and Finn et al. (2016) on the Human 3.6M dataset.