Learning Consistent Visual Synthesis

With the rapid development of photography, we can easily record the 3D world by taking photos and videos. In traditional images and videos, the viewer observes the scene from fixed viewpoints and cannot navigate the scene or edit the 2D observation afterward. Thus, visual content editing and synthesis become an essential task in computer vision. However, achieving high-quality visual synthesis often requires a complex and expensive multi-camera setup. This is not practical for daily use because most people only have one cellphone camera. But a single camera, on the contrary, could not provide enough multi-view constraints to synthesize consistent visual content. Therefore, in this thesis, I address this challenging single-camera visual synthesis problem by leveraging different regularizations. I study three consistent synthesis problems: time-consistent synthesis, view-consistent synthesis, and view-time-consistent synthesis. I show how we can take cellphone-captured monocular images and videos as input to model the scene and consistently synthesize new content for an immersive viewing experience.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Gao, Chen

Chair dc:contributor.committeechair

• Huang, Jia-Bin

Committee members dc:contributor.committeemember

• Dhillon, Harpreet Singh
• Kopf, Johannes Peter
• Huang, Bert
• Abbott, A. Lynn

• Computer vision
• Computational photography
• View synthesis
• Temporal consistency

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:35023

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/111588