Controllable Visual Synthesis

AlBahar, Badour A. Sh A.

Controllable Visual Synthesis

dc.contributor.author	AlBahar, Badour A. Sh A.	en
dc.contributor.committeechair	Abbott, Amos L.	en
dc.contributor.committeechair	Huang, Jia-Bin	en
dc.contributor.committeemember	Dhillon, Harpreet Singh	en
dc.contributor.committeemember	Lourentzou, Ismini	en
dc.contributor.committeemember	Jia, Ruoxi	en
dc.contributor.committeemember	Wang, Ting-Chun	en
dc.contributor.department	Electrical and Computer Engineering	en
dc.date.accessioned	2023-06-09T08:00:25Z	en
dc.date.available	2023-06-09T08:00:25Z	en
dc.date.issued	2023-06-08	en
dc.description.abstract	Computer graphics has become an integral part of various industries such as entertainment (i.e.,films and content creation), fashion (i.e.,virtual try-on), and video games. Computer graphics has evolved tremendously over the past years. It has shown remarkable image generation improvement from low-quality, pixelated images with limited details to highly realistic images with fine details that can often be mistaken for real images. However, the traditional pipeline of rendering an image in computer graphics is complex and time- consuming. The whole process of creating the geometry, material, and textures requires not only time but also significant expertise. In this work, we aim to replace this complex traditional computer graphics pipeline with a simple machine learning model. This machine learning model can synthesize realistic images without requiring expertise or significant time and effort. Specifically, we address the problem of controllable image synthesis. We propose several approaches that allow the user to synthesize realistic content and manipulate images to achieve their desired goals with ease and flexibility.	en
dc.description.abstractgeneral	Computer graphics has become an integral part of various industries such as entertainment (i.e.,films and content creation), fashion (i.e.,virtual try-on), and video games. Computer graphics has evolved tremendously over the past years. It has shown remarkable image generation improvement from low-quality, pixelated images with limited details to highly realistic images with fine details that can often be mistaken for real images. However, the traditional process of generating an image in computer graphics is complex and time- consuming. You need to set up a camera and light, and create objects with all sorts of details. This requires not only time but also significant expertise. In this work, we aim to replace this complex traditional computer graphics pipeline with a simple machine learning model. This machine learning model can generate realistic images without requiring expertise or significant time and effort. Specifically, we address the problem of controllable image synthesis. We propose several approaches that allow the user to synthesize realistic content and manipulate images to achieve their desired goals with ease and flexibility.	en
dc.description.degree	Doctor of Philosophy	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:37466	en
dc.identifier.uri	http://hdl.handle.net/10919/115382	en
dc.language.iso	en	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Computer vision	en
dc.subject	Computer graphic	en
dc.subject	Image-to-image translation	en
dc.subject	Pose transfer	en
dc.subject	Human reposing	en
dc.subject	Video editing	en
dc.title	Controllable Visual Synthesis	en
dc.type	Dissertation	en
thesis.degree.discipline	Computer Engineering	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Doctor of Philosophy	en