Neurosymbolic Models for Computer Graphics.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Neurosymbolic Models for Computer Graphics.
Συγγραφείς:	Ritchie, Daniel, Guerrero, Paul, Jones, R. Kenny, Mitra, Niloy J., Schulz, Adriana, Willis, Karl D. D., Wu, Jiajun
Πηγή:	Computer Graphics Forum; May2023, Vol. 42 Issue 2, p545-568, 24p, 5 Color Photographs, 12 Diagrams, 4 Charts
Θεματικοί όροι:	Computer simulation, Machine learning, Artificial intelligence, Materials texture, Computer vision, Computer graphics
Περίληψη:	Procedural models (i.e. symbolic programs that output visual data) are a historically‐popular method for representing graphics content: vegetation, buildings, textures, etc. They offer many advantages: interpretable design parameters, stochastic variations, high‐quality outputs, compact representation, and more. But they also have some limitations, such as the difficulty of authoring a procedural model from scratch. More recently, AI‐based methods, and especially neural networks, have become popular for creating graphic content. These techniques allow users to directly specify desired properties of the artifact they want to create (via examples, constraints, or objectives), while a search, optimization, or learning algorithm takes care of the details. However, this ease of use comes at a cost, as it's often hard to interpret or manipulate these representations. In this state‐of‐the‐art report, we summarize research on neurosymbolic models in computer graphics: methods that combine the strengths of both AI and symbolic programs to represent, generate, and manipulate visual data. We survey recent work applying these techniques to represent 2D shapes, 3D shapes, and materials & textures. Along the way, we situate each prior work in a unified design space for neurosymbolic models, which helps reveal underexplored areas and opportunities for future research. [ABSTRACT FROM AUTHOR]
	Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων:	Complementary Index

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Header	DbId: edb DbLabel: Complementary Index An: 163874373 RelevancyScore: 932 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 931.955322265625
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Items	– Name: Title Label: Title Group: Ti Data: Neurosymbolic Models for Computer Graphics. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ritchie%2C+Daniel%22">Ritchie, Daniel</searchLink><br /><searchLink fieldCode="AR" term="%22Guerrero%2C+Paul%22">Guerrero, Paul</searchLink><br /><searchLink fieldCode="AR" term="%22Jones%2C+R%2E+Kenny%22">Jones, R. Kenny</searchLink><br /><searchLink fieldCode="AR" term="%22Mitra%2C+Niloy+J%2E%22">Mitra, Niloy J.</searchLink><br /><searchLink fieldCode="AR" term="%22Schulz%2C+Adriana%22">Schulz, Adriana</searchLink><br /><searchLink fieldCode="AR" term="%22Willis%2C+Karl+D%2E+D%2E%22">Willis, Karl D. D.</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Jiajun%22">Wu, Jiajun</searchLink> – Name: TitleSource Label: Source Group: Src Data: Computer Graphics Forum; May2023, Vol. 42 Issue 2, p545-568, 24p, 5 Color Photographs, 12 Diagrams, 4 Charts – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Materials+texture%22">Materials texture</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+graphics%22">Computer graphics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Procedural models (i.e. symbolic programs that output visual data) are a historically‐popular method for representing graphics content: vegetation, buildings, textures, etc. They offer many advantages: interpretable design parameters, stochastic variations, high‐quality outputs, compact representation, and more. But they also have some limitations, such as the difficulty of authoring a procedural model from scratch. More recently, AI‐based methods, and especially neural networks, have become popular for creating graphic content. These techniques allow users to directly specify desired properties of the artifact they want to create (via examples, constraints, or objectives), while a search, optimization, or learning algorithm takes care of the details. However, this ease of use comes at a cost, as it's often hard to interpret or manipulate these representations. In this state‐of‐the‐art report, we summarize research on neurosymbolic models in computer graphics: methods that combine the strengths of both AI and symbolic programs to represent, generate, and manipulate visual data. We survey recent work applying these techniques to represent 2D shapes, 3D shapes, and materials & textures. Along the way, we situate each prior work in a unified design space for neurosymbolic models, which helps reveal underexplored areas and opportunities for future research. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo	BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/cgf.14775 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 545 Subjects: – SubjectFull: Computer simulation Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Materials texture Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Computer graphics Type: general Titles: – TitleFull: Neurosymbolic Models for Computer Graphics. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ritchie, Daniel – PersonEntity: Name: NameFull: Guerrero, Paul – PersonEntity: Name: NameFull: Jones, R. Kenny – PersonEntity: Name: NameFull: Mitra, Niloy J. – PersonEntity: Name: NameFull: Schulz, Adriana – PersonEntity: Name: NameFull: Willis, Karl D. D. – PersonEntity: Name: NameFull: Wu, Jiajun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 01677055 Numbering: – Type: volume Value: 42 – Type: issue Value: 2 Titles: – TitleFull: Computer Graphics Forum Type: main
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