We gratefully acknowledge support from
the Simons Foundation
and member institutions
Full-text links:

Download:

Current browse context:

cs.CV

Change to browse by:

cs

References & Citations

Bookmark

(what is this?)
CiteULike logo BibSonomy logo Mendeley logo del.icio.us logo Digg logo Reddit logo ScienceWISE logo

Computer Science > Computer Vision and Pattern Recognition

Title: Dual Motion GAN for Future-Flow Embedded Video Prediction

Abstract: Future frame prediction in videos is a promising avenue for unsupervised video representation learning. Video frames are naturally generated by the inherent pixel flows from preceding frames based on the appearance and motion dynamics in the video. However, existing methods focus on directly hallucinating pixel values, resulting in blurry predictions. In this paper, we develop a dual motion Generative Adversarial Net (GAN) architecture, which learns to explicitly enforce future-frame predictions to be consistent with the pixel-wise flows in the video through a dual-learning mechanism. The primal future-frame prediction and dual future-flow prediction form a closed loop, generating informative feedback signals to each other for better video prediction. To make both synthesized future frames and flows indistinguishable from reality, a dual adversarial training method is proposed to ensure that the future-flow prediction is able to help infer realistic future-frames, while the future-frame prediction in turn leads to realistic optical flows. Our dual motion GAN also handles natural motion uncertainty in different pixel locations with a new probabilistic motion encoder, which is based on variational autoencoders. Extensive experiments demonstrate that the proposed dual motion GAN significantly outperforms state-of-the-art approaches on synthesizing new video frames and predicting future flows. Our model generalizes well across diverse visual scenes and shows superiority in unsupervised video representation learning.
Comments: ICCV 17 camera ready
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:1708.00284 [cs.CV]
  (or arXiv:1708.00284v2 [cs.CV] for this version)

Submission history

From: Xiaodan Liang [view email]
[v1] Tue, 1 Aug 2017 12:38:58 GMT (2932kb,D)
[v2] Thu, 3 Aug 2017 04:23:30 GMT (2932kb,D)