# Improved Conditional VRNNs for Video Prediction

## TL;DR

This paper introduces an enhanced hierarchical variational autoencoder framework with more expressive latent distributions and higher capacity likelihood models to improve the accuracy and quality of video frame prediction.

## Contribution

It proposes a novel hierarchical latent variable model that increases the expressiveness of the latent space for better future frame prediction in videos.

## Key findings

- Outperforms current state-of-the-art models on multiple datasets.
- Produces sharper and more accurate future video frames.
- Demonstrates the effectiveness of hierarchical latent structures.

## Abstract

Predicting future frames for a video sequence is a challenging generative modeling task. Promising approaches include probabilistic latent variable models such as the Variational Auto-Encoder. While VAEs can handle uncertainty and model multiple possible future outcomes, they have a tendency to produce blurry predictions. In this work we argue that this is a sign of underfitting. To address this issue, we propose to increase the expressiveness of the latent distributions and to use higher capacity likelihood models. Our approach relies on a hierarchy of latent variables, which defines a family of flexible prior and posterior distributions in order to better model the probability of future sequences. We validate our proposal through a series of ablation experiments and compare our approach to current state-of-the-art latent variable models. Our method performs favorably under several metrics in three different datasets.