MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Generating the Future with Adversarial Transformers

Author(s)
Vondrick, Carl Martin; Torralba, Antonio
Thumbnail
DownloadAccepted version (4.740Mb)
Terms of use
Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
Metadata
Show full item record
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
Date issued
2017-11-09
URI
https://hdl.handle.net/1721.1/123483
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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
ISBN
9781538604571
9781538604588
ISSN
1063-6919

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.