{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/127441"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/127441","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Light source relighting for indoor scene photos with deep neural networks","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Peng, Anthony Bo."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Light source relighting for indoor scene photos with deep neural networks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Antonio Torralba."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"],"dc:contributor.other":["Massachusetts Institute of Technology. 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