Structured representation learning by controlling generative models
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: He, Xingzhe
issued: 2024-03-06T17:18:50Z
issued: 2024
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Object correspondence and structure play critical roles in image generation, 3D reconstruction and animation. In recent years, supervised algorithms have dramatically improved the accuracy of the learned correspondence. However, these approaches are expensive due to manual annotation and do not generalize well to new domains. We propose several methods in this dissertation on unsupervised structure learning from casually recorded images and videos. To be specific, we propose a Generative Adversarial Network (GAN)-based unsupervised keypoint detector and extend it for object part segmentation. Furthermore, we introduce a representation for unsupervised keypoints relationship estimation. We later adapted this technique for few-shot keypoint learning, depth prediction, and occlusion handling. In addition, we propose a dataset generation approach for diffusion model personalization to implicitly learn the object structure and appearance. The overarching goal of this dissertation is to make progress in the field of unsupervised object correspondence and structure learning. Our proposed methods outperform existing unsupervised methods on the established keypoint estimation and part segmentation benchmarks and paves the way for structure-conditioned generative models on more diverse datasets.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/87531
note: Structured Representation Learning byControlling Generative ModelsbyXingzhe HeB.Sc., Xi’an Jiaotong-Liverpool University, 2017B.Sc., The University of Liverpool, 2017M.Sc., Rutgers, The State University of New Jersey, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)March 2024© Xingzhe He 2024The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the thesis entitled:Structured Representation Learning by Controlling Generative Modelssubmitted by Xingzhe He in partial fulfillment of the requirements forthe degree of Doctor of Philosophyin Computer Science .Examining Committee:Helge Rhodin, Assistant Professor, Department of Computer Science, UBCSupervisorLeonid Sigal, Associate Professor, Department of Computer Science, UBCSupervisory Committee MembersKwang Moo Yi, Assistant Professor, Department of Computer Science, UBCSupervisory Committee MembersDavid Poole, Professor, Department of Computer Science, UBCUniversity ExaminerTrevor Campbell, Associate Professor, Department of Statistics, UBCUniversity ExaminerAndrea Vedaldi, Professor, Department of Engineering Science, University of OxfordExternal ExamineriiAbstractObject correspondence and structure play critical roles in image generation, 3D reconstruc-tion and animation. In recent years, supervised algorithms have dramatically improved theaccuracy of the learned correspondence. However, these approaches are expensive due tomanual annotation and do not generalize well to new domains. We propose several meth-ods in this dissertation on unsupervised structure learning from casually recorded imagesand videos. To be specific, we propose a Generative Adversarial Network (GAN)-basedunsupervised keypoint detector and extend it for object part segmentation. Furthermore,we introduc…
has type: Thesis/Dissertation
date issued: 2024-05
is shown at: 10.14288/1.0440629
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Rhodin, Helge
type: Text
identifier URI: http://hdl.handle.net/2429/87531