Light source relighting for indoor scene photos with deep neural networks

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Peng, Anthony Bo.

Advisor dc:contributor.advisor

• Antonio Torralba.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127441

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127441