# One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network

arXiv: https://arxiv.org/abs/1702.04125 · HTML: https://mlsanity.com/paper/1702.04125/

## Key facts

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

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

- [Objects do not disappear: Video object detection by single-frame object location anticipation](https://mlsanity.com/paper/2308.04770/) (2023)
- [S-HR-VQVAE: Sequential Hierarchical Residual Learning Vector Quantized Variational Autoencoder for Video Prediction](https://mlsanity.com/paper/2307.06701/) (2023)
- [Dense Semantic Forecasting in Video by Joint Regression of Features and Feature Motion](https://mlsanity.com/paper/2101.10777/) (2021)
- [Spatiotemporal downscaling and nowcasting of urban land surface temperatures with deep neural networks](https://mlsanity.com/paper/2605.13566/) (2026)

## Abstract (arXiv)

There is an inherent need for autonomous cars, drones, and other robots to have a notion of how their environment behaves and to anticipate changes in the near future. In this work, we focus on anticipating future appearance given the current frame of a video. Existing work focuses on either predicting the future appearance as the next frame of a video, or predicting future motion as optical flow or motion trajectories starting from a single video frame. This work stretches the ability of CNNs (Convolutional Neural Networks) to predict an anticipation of appearance at an arbitrarily given future time, not necessarily the next video frame. We condition our predicted future appearance on a continuous time variable that allows us to anticipate future frames at a given temporal distance, directly from the input video frame. We show that CNNs can learn an intrinsic representation of typical appearance changes over time and successfully generate realistic predictions at a deliberate time difference in the near future.