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Photorealistic Digital Content Creation for Extended Reality From Sparse In-the-Wild Images
Photorealistic Digital Content Creation for Extended Reality From Sparse In-the-Wild Images
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151347
- ISBN
- 9798383188507
- DDC
- 004
- 저자명
- Yeh, Yu-Ying.
- 서명/저자
- Photorealistic Digital Content Creation for Extended Reality From Sparse In-the-Wild Images
- 발행사항
- [Sl] : University of California, San Diego, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 180 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
- 주기사항
- Advisor: Chandraker, Manmohan.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- 초록/해제
- 요약Extended Reality (XR) encompasses immersive technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), which blend the physical and digital worlds. For XR experiences to captivate users and seamlessly integrate with reality, photorealistic content is essential. Photorealism ensures that virtual elements convincingly interact with real-world environments, enhancing immersion and fostering a sense of presence for users. This dissertation explores various methods to facilitate the convenient and efficient creation of photorealistic digital content from images for diverse subjects.Creating photorealistic content from images involves estimating intrinsic components from scenes, a highly challenging and ill-posed problem. To ensure photorealism, this dissertation focuses on synthesizing spatially-varying Bidirectional Reflectance Distribution Functions (BRDFs) or textures, modeling complex light transport, and leveraging large-scale real-world data. Firstly, we discuss how existing priors can be used for material and lighting transfer from images to 3D scene geometry. Secondly, we explore the utilization of diffusion models pre-trained on large-scale real-world images as priors for high-quality texture synthesis and transfer to arbitrary 3D shapes with image inputs. Additionally, specialized objects like transparent shapes or portraits are addressed through learning-based approaches with synthetic data and synthetic-to-real adaptation for complex light transport and relighting to handle specific appearances.The key contribution of this dissertation is developing efficient methods to create high-quality photorealistic content for XR with minimal human effort. Unlike prior works that depend on elaborate capture systems or extensive image sets, this dissertation achieves photorealism using just a few images easily captured from commercial mobile devices. We demonstrate diverse, high-quality photorealistic content produced by our methods, suitable for various XR applications.
- 일반주제명
- Computer science
- 일반주제명
- Information technology
- 키워드
- Computer vision
- 키워드
- Digital contents
- 기타저자
- University of California, San Diego Computer Science and Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■007cr#unu||||||||
■020 ▼a9798383188507
■035 ▼a(MiAaPQ)AAI31242738
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aYeh, Yu-Ying.
■24510▼aPhotorealistic Digital Content Creation for Extended Reality From Sparse In-the-Wild Images
■260 ▼a[Sl]▼bUniversity of California, San Diego▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a180 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-01, Section: B.
■500 ▼aAdvisor: Chandraker, Manmohan.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2024.
■520 ▼aExtended Reality (XR) encompasses immersive technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), which blend the physical and digital worlds. For XR experiences to captivate users and seamlessly integrate with reality, photorealistic content is essential. Photorealism ensures that virtual elements convincingly interact with real-world environments, enhancing immersion and fostering a sense of presence for users. This dissertation explores various methods to facilitate the convenient and efficient creation of photorealistic digital content from images for diverse subjects.Creating photorealistic content from images involves estimating intrinsic components from scenes, a highly challenging and ill-posed problem. To ensure photorealism, this dissertation focuses on synthesizing spatially-varying Bidirectional Reflectance Distribution Functions (BRDFs) or textures, modeling complex light transport, and leveraging large-scale real-world data. Firstly, we discuss how existing priors can be used for material and lighting transfer from images to 3D scene geometry. Secondly, we explore the utilization of diffusion models pre-trained on large-scale real-world images as priors for high-quality texture synthesis and transfer to arbitrary 3D shapes with image inputs. Additionally, specialized objects like transparent shapes or portraits are addressed through learning-based approaches with synthetic data and synthetic-to-real adaptation for complex light transport and relighting to handle specific appearances.The key contribution of this dissertation is developing efficient methods to create high-quality photorealistic content for XR with minimal human effort. Unlike prior works that depend on elaborate capture systems or extensive image sets, this dissertation achieves photorealism using just a few images easily captured from commercial mobile devices. We demonstrate diverse, high-quality photorealistic content produced by our methods, suitable for various XR applications.
■590 ▼aSchool code: 0033.
■650 4▼aComputer science
■650 4▼aInformation technology
■653 ▼a3D content creation
■653 ▼aComputer graphics
■653 ▼aComputer vision
■653 ▼aInverse rendering
■653 ▼aDigital contents
■690 ▼a0984
■690 ▼a0489
■690 ▼a0800
■71020▼aUniversity of California, San Diego▼bComputer Science and Engineering.
■7730 ▼tDissertations Abstracts International▼g86-01B.
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161370▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
