Generating the Future with Adversarial Transformers
Name
transformer.pdf
Description
Accepted version
Size
4.74 MB
Format
Adobe PDF
Checksum (MD5)
53911734bdce43693a9bfb9abe995caf
Author(s)
Vondrick, Carl Martin
Torralba, Antonio
Date Issued
November 9, 2017
Journal
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Citation
Vondrick, Carl, and Antonio Torralba. "Generating the Future with Adversarial Transformers." 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017, Honolulu, Hawaii, USA, IEEE, 2017
Version
Author's final manuscript
Abstract
We learn models to generate the immediate future in video. This problem has two main challenges. Firstly, since the future is uncertain, models should be multi-modal, which can be difficult to learn. Secondly, since the future is similar to the past, models store low-level details, which complicates learning of high-level semantics. We propose a framework to tackle both of these challenges. We present a model that generates the future by transforming pixels in the past. Our approach explicitly disentangles the model's memory from the prediction, which helps the model learn desirable invariances. Experiments suggest that this model can generate short videos of plausible futures. We believe predictive models have many applications in robotics, health-care, and video understanding. Keywords: predictive models; generators; visualization; network architecture; spatial resolution; semantics; robots
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cvpr.2017.319
