Light source relighting for indoor scene photos with deep neural networks
Name
1192966286-MIT.pdf
Size
16.49 MB
Format
Adobe PDF
Checksum (MD5)
93734e5b59501ece491c2ac0cfdf19b2
Author(s)
Peng, Anthony Bo.
Advisor(s)
Antonio Torralba.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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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.
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