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Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

Xue, Tianfan and Wu, Jiajun and Bouman, Katherine L. and Freeman, William T. (2016) Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks. In: Advances in Neural Information Processing Systems (NIPS 2016). Neural Information Processing Systems Foundation , La Jolla, CA. ISBN 9781510838819. http://resolver.caltech.edu/CaltechAUTHORS:20190405-161634029

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Abstract

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or non-parametric way, we propose a novel approach which models future frames in a probabilistic manner. Our proposed method is therefore able to synthesize multiple possible next frames using the same model. Solving this challenging problem involves low- and high-level image and motion understanding for successful image synthesis. Here, we propose a novel network structure, namely a Cross Convolutional Network, that encodes images as feature maps and motion information as convolutional kernels to aid in synthesizing future frames. In experiments, our model performs well on both synthetic data, such as 2D shapes and animated game sprites, as well as on real-wold video data. We show that our model can also be applied to tasks such as visual analogy-making, and present analysis of the learned network representations.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://papers.nips.cc/paper/6552-visual-dynamics-probabilistic-future-frame-synthesis-via-cross-convolutional-networksPublisherArticle
http://arxiv.org/abs/1607.02586arXivArticle
ORCID:
AuthorORCID
Bouman, Katherine L.0000-0003-0077-4367
Freeman, William T.0000-0002-2231-7995
Additional Information:© 2016 Neural Information Processing Systems Foundation. The authors thank Yining Wang for helpful discussions. This work is supported by NSF Robust Intelligence 1212849, NSF Big Data 1447476, ONR MURI 6923196, Adobe, and Shell Research. The authors would also like to thank Nvidia for GPU donation. The first two authors contributed equally to this work.
Funders:
Funding AgencyGrant Number
NSFIIS-1212849
UNSPECIFIEDIIS-1447476
Office of Naval Research6923196
AdobeUNSPECIFIED
Shell ResearchUNSPECIFIED
nVidiaUNSPECIFIED
Record Number:CaltechAUTHORS:20190405-161634029
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190405-161634029
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94522
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:09 Apr 2019 15:25
Last Modified:09 Apr 2019 15:25

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