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FASTR : fast approximation of soft tissue in real time Liang, Ziheng
Abstract
Real-time animations are limited by a computational budget and often trade realism for performance. Simulating the shape of a realistic human body requires a tremendous amount of computational resources and may be infeasible in real-time applications. In recent years, deep learning approaches have proven their effectiveness in fields such as computer vision. We present a new method that combines ideas from deep learning and example-based skinning. The method approximates corrections to skin deformation from a skeleton-based animation baseline. A key aspect of the approach is to factor the network into two parts, with part of the network evaluated using shaders in the standard real-time graphics rendering pipeline. Our method adds a minimum overhead to a skeleton-based animation while improving its visual results.
Item Metadata
Title |
FASTR : fast approximation of soft tissue in real time
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Creator | |
Supervisor | |
Publisher |
University of British Columbia
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Date Issued |
2022
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Description |
Real-time animations are limited by a computational budget and often trade realism for performance. Simulating the shape of a realistic human body requires a tremendous amount of computational resources and may be infeasible in real-time applications. In recent years, deep learning approaches have proven their effectiveness in fields such as computer vision. We present a new method that combines ideas from deep learning and example-based skinning. The method approximates corrections to skin deformation from a skeleton-based animation baseline. A key aspect of the approach is to factor the network into two parts, with part of the network evaluated using shaders in the standard real-time graphics rendering pipeline. Our method adds a minimum overhead to a skeleton-based animation while improving its visual results.
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Genre | |
Type | |
Language |
eng
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Date Available |
2023-04-30
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0412965
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2022-05
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International