# Learning Temporal Transformations From Time-Lapse Videos

arXiv: https://arxiv.org/abs/1608.07724 · HTML: https://mlsanity.com/paper/1608.07724/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2016.

## Built on by (most cited first, 7 of 7)

- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos](https://mlsanity.com/paper/2203.11637/) (2022)
- [A survey on GANs for computer vision: Recent research, analysis and taxonomy](https://mlsanity.com/paper/2203.11242/) (2022)
- [Comparison and Analysis of Image-to-Image Generative Adversarial Networks: A Survey](https://mlsanity.com/paper/2112.12625/) (2021)
- [Self Sparse Generative Adversarial Networks](https://mlsanity.com/paper/2101.10556/) (2021)
- [Tensor-to-Image: Image-to-Image Translation with Vision Transformers](https://mlsanity.com/paper/2110.08037/) (2021)
- [Enhancing Bandwidth Efficiency for Video Motion Transfer Applications using Deep Learning Based Keypoint Prediction](https://mlsanity.com/paper/2403.11337/) (2024)

## Abstract (arXiv)

Based on life-long observations of physical, chemical, and biologic phenomena in the natural world, humans can often easily picture in their minds what an object will look like in the future. But, what about computers? In this paper, we learn computational models of object transformations from time-lapse videos. In particular, we explore the use of generative models to create depictions of objects at future times. These models explore several different prediction tasks: generating a future state given a single depiction of an object, generating a future state given two depictions of an object at different times, and generating future states recursively in a recurrent framework. We provide both qualitative and quantitative evaluations of the generated results, and also conduct a human evaluation to compare variations of our models.