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Light source relighting for indoor scene photos with deep neural networks

Author(s)
Peng, Anthony Bo.
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Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Antonio Torralba.
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
We seek to use deep neural networks to develop a method to detect the light sources in a given image of an indoor scene, computationally adjust their lighting intensity, and re-render the edited scene as an image. By doing so, we can visually relight the image--effectively turning the light source "on" or "off" in the image. This thesis introduces such a method by using Generative Adversarial Networks (GANs) and intervention techniques to this end. The method is composed of a pipeline of processing stages, from detecting the light sources to reconstructing the scene in GAN representation space to performing edits on the GAN representation to fine-grained control over the edited lighting, and we present its results here. The thesis work has a wide range of applications in the field of content creation and image editing.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
 
Cataloged from the official PDF of thesis.
 
Includes bibliographical references (pages 67-69).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/127441
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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