Natural video synthesis with Generative Adversarial Networks
Name
1127639631-MIT.pdf
Size
18.16 MB
Format
Adobe PDF
Checksum (MD5)
2243d482cf3d6be39021530df8bf7df8
Author(s)
Egan, Nicholas R.(Nicholas Ryan)
Advisor(s)
Antonio Torralba.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Generative Adversarial Networks (GANs) are the state of the art neural network models for image generation, but the use of GANs for video generation is still largely unexplored. This thesis introduces new GAN based video generation methods by proposing the technique of model inflation and the segmentation-to-video task. The model inflation technique converts image generative models into video generative models, and experiments show that model inflation improves training speed, training stability, and output video quality. The segmentation-to-video task is that of turning an input image segmentation mask into an output video matching that segmentation. A GAN model was created to perform this task, and its usefulness as a creative tool was demonstrated.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 71-74).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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