A Data-Driven Approach to Realistic Shape Morphing

Morphing between 3D objects is a fundamental technique in computer graphics. Traditional methods of shape morphing focus on establishing meaningful correspondences and finding smooth interpolation between shapes. Such methods however only take geometric information as input and thus cannot in genera...

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Bibliographic Details
Published in:Computer graphics forum Vol. 32; no. 2pt4; pp. 449 - 457
Main Authors: Gao, Lin, Lai, Yu-Kun, Huang, Qi-Xing, Hu, Shi-Min
Format: Journal Article
Language:English
Published: Oxford, UK Blackwell Publishing Ltd 01.05.2013
Subjects:
3-D graphics
Analysis
Categories
Computer graphics
Deformation
I.3.5 [Computer Graphics]: Computational Geometry and Object Modelling-object representations
Image processing systems
Interpolation
Morphing
Optimization
Studies
Three dimensional
Topological manifolds
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:Morphing between 3D objects is a fundamental technique in computer graphics. Traditional methods of shape morphing focus on establishing meaningful correspondences and finding smooth interpolation between shapes. Such methods however only take geometric information as input and thus cannot in general avoid producing unnatural interpolation, in particular for large‐scale deformations. This paper proposes a novel data‐driven approach for shape morphing. Given a database with various models belonging to the same category, we treat them as data samples in the plausible deformation space. These models are then clustered to form local shape spaces of plausible deformations. We use a simple metric to reasonably represent the closeness between pairs of models. Given source and target models, the morphing problem is casted as a global optimization problem of finding a minimal distance path within the local shape spaces connecting these models. Under the guidance of intermediate models in the path, an extended as‐rigid‐as‐possible interpolation is used to produce the final morphing. By exploiting the knowledge of plausible models, our approach produces realistic morphing for challenging cases as demonstrated by various examples in the paper.
Bibliography:ark:/67375/WNG-6RKF6K2L-K
ArticleID:CGF12065
istex:F9C6E69160356E9922710292B263EEAE11548811
Emaill
gaolinorange@gmail.com
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12065