# Geometry-Based Next Frame Prediction from Monocular Video

arXiv: https://arxiv.org/abs/1609.06377 · HTML: https://mlsanity.com/paper/1609.06377/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2016.

## Built on by (most cited first, 6 of 6)

- [Imitation Learning for Human Pose Prediction](https://mlsanity.com/paper/1909.03449/) (2019)
- [Forecasting of depth and ego-motion with transformers and self-supervision](https://mlsanity.com/paper/2206.07435/) (2022)
- [Self-Guided Instance-Aware Network for Depth Completion and Enhancement](https://mlsanity.com/paper/2105.12186/) (2021)
- [STDepthFormer: Predicting Spatio-temporal Depth from Video with a Self-supervised Transformer Model](https://mlsanity.com/paper/2303.01196/) (2023)
- [Cognitively Inspired Energy-Based World Models](https://mlsanity.com/paper/2406.08862/) (2024)
- [FREGAN : an application of generative adversarial networks in enhancing the frame rate of videos](https://mlsanity.com/paper/2111.01105/) (2021)

## Abstract (arXiv)

We consider the problem of next frame prediction from video input. A recurrent convolutional neural network is trained to predict depth from monocular video input, which, along with the current video image and the camera trajectory, can then be used to compute the next frame. Unlike prior next-frame prediction approaches, we take advantage of the scene geometry and use the predicted depth for generating the next frame prediction. Our approach can produce rich next frame predictions which include depth information attached to each pixel. Another novel aspect of our approach is that it predicts depth from a sequence of images (e.g. in a video), rather than from a single still image. We evaluate the proposed approach on the KITTI dataset, a standard dataset for benchmarking tasks relevant to autonomous driving. The proposed method produces results which are visually and numerically superior to existing methods that directly predict the next frame. We show that the accuracy of depth prediction improves as more prior frames are considered.