Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures

Mittal, Gaurav and Marwah, Tanya and Balasubramanian, Vineeth N (2017) Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures. arXiv. pp. 1-9.

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is paper introduces a novel approach for generating videos called Synchronized Deep Recurrent A�entive Writer (Sync-DRAW). Sync- DRAW can also perform text-to-video generation which, to the best of our knowledge, makes it the �rst approach of its kind. It com- bines a Variational Autoencoder (VAE) with a Recurrent A�ention Mechanism in a novel manner to create a temporally dependent sequence of frames that are gradually formed over time. �e recur- rent a�ention mechanism in Sync-DRAW a�ends to each individual frame of the video in sychronization, while the VAE learns a latent distribution for the entire video at the global level. Our experiments with Bouncing MNIST, KTH and UCF-101 suggest that Sync-DRAW is e�cient in learning the spatial and temporal information of the videos and generates frames with high structural integrity, and can generate videos from simple captions on these datasets.

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IITH Creators:
IITH CreatorsORCiD
Balasubramanian, Vineeth NUNSPECIFIED
Item Type: Article
Subjects: Computer science > Special computer methods
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 12 Oct 2017 11:05
Last Modified: 12 Oct 2017 11:05
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