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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| Veröffentlicht in: | Computer graphics forum Jg. 32; H. 2pt4; S. 449 - 457 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Oxford, UK
Blackwell Publishing Ltd
01.05.2013
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | 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. |
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| AbstractList | 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. [PUBLICATION ABSTRACT] 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. |
| Author | Lai, Yu-Kun Huang, Qi-Xing Gao, Lin Hu, Shi-Min |
| Author_xml | – sequence: 1 givenname: Lin surname: Gao fullname: Gao, Lin organization: TNList, Department of Computer Science and Technology, Tsinghua University, China – sequence: 2 givenname: Yu-Kun surname: Lai fullname: Lai, Yu-Kun organization: School of Computer Science and Informatics, Cardiff University, UK – sequence: 3 givenname: Qi-Xing surname: Huang fullname: Huang, Qi-Xing organization: Computer Science Department, Stanford University, USA – sequence: 4 givenname: Shi-Min surname: Hu fullname: Hu, Shi-Min organization: TNList, Department of Computer Science and Technology, Tsinghua University, China |
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| References_xml | – reference: Weber O., Sorkine O., Lipman Y., Gotsman C.: Context-aware skeletal shape deformation. Comput. Graph. Forum 26, 3 (2007), 265-273. 2. – reference: Xu D., Zhang H., Wang Q., Bao H.: Poisson shape interpolation. Graph. Models 68, 3 (2006), 268-281. 2, 6, 7, 8. – reference: Kin-Chung Au O., Tai C.-L., Liu L., Fu H.: Dual Laplacian editing for meshes. IEEE Trans. Vis. Comp. Graph. 12, 3 (2006), 386-395. 2. – reference: Lee T.-Y., Huang P.-H.: Fast and intuitive metamorphosis of 3D polyhedral models using smcc mesh merging scheme. IEEE Trans. Vis. Comp. Graph. 9 (2003), 85-98. 2. – reference: Anguelov D., Srinivasan P., Koller D., Thrun S., Rodgers J., Davis J.: SCAPE: shape completion and animation of people. ACM Trans. Graph. 24, 3 (2005), 408-416. 3, 6. – reference: Kilian M., Mitra N. J., Pottmann H.: Geometric modeling in shape space. ACM Trans. Graph. 26, 3 (2007), 64:1-8. 2. – reference: Winkler T., Drieseberg J., Alexa M., Hormann K.: Multi-scale geometry interpolation. Comput. Graph. Forum 29, 2 (2010), 309-318. 2. – reference: Allen B., Curless B., Popovic Z.: The space of human body shapes: reconstruction and parameterization from range scans. ACM Trans. Graph. 22, 3 (2003), 587-594. 3. – reference: Wang C. C.: Parameterization and parametric design of mannequins. Computer-Aided Design 37, 1 (2005), 83-98. 3. – reference: Kircher S., Garland M.: Free-form motion processing. ACM Trans. Graph. 27, 2 (2008), 12:1-13. 8. – reference: Sumner R. W., Popovic′ J.: Deformation transfer for triangle meshes. ACM Trans. Graph. 23, 3 (2004), 399-405. 2, 3, 6, 7. – reference: Cashman T. J., Hormann K.: A continuous, editable representation for deforming mesh sequences with separate signals for time, pose and shape. Comput. Graph. Forum 31, 2 (2012), 735-744. 8. – reference: Yan H.-B., Hu S.-M., Martin R. R.: 3D morphing using strain field interpolation. J. Comput. Sci. Technol. 22, 1 (2007), 147-155. 2. – reference: Baran I., Vlasic D., Grinspun E., Popović J.: Semantic deformation transfer. ACM Trans. Graph. 28, 3 (2009), 36:1-6. 3. – reference: Lin C.-H., Lee T.-Y.: Metamorphosis of 3D polyhedral models using progressive connectivity transformations. IEEE Trans. Vis. Comp. Graph. 11, 1 (2005), 2-12. 2. – reference: Frey B. J., Dueck D.: Clustering by passing messages between data points. Science 315 (2007), 972-976. 4. – reference: Lipman Y., Sorkine O., Levin D., Cohen-Or D.: Linear rotation-invariant coordinates for meshes. ACM Trans. Graph. 24, 3 (2005), 479-487. 2. – reference: Vlasic D., Baran I., Matusik W., Popovic′ J.: Articulated mesh animation from multi-view silhouettes. ACM Trans. Graph. 27, 3 (2008), 97:1-9. 3, 7. – reference: Lipman Y., Funkhouser T.: Möbius voting for surface correspondence. ACM Trans. Graph. 28, 3 (2009), 72:1-12. 3. – reference: Grochow K., Martin S. L., Hertzmann A., Popović Z.: Style-based inverse kinematics. ACM Trans. Graph. 23, 3 (2004), 522-531. 2. – reference: Kim V. G., Lipman Y., Funkhouser T.: Blended intrinsic maps. ACM Trans. Graph. 30, 4 (2011), 79:1-12. 3, 6. – reference: Seo H., Magnenat-Thalmann N.: An example-based approach to human body manipulation. Graph. Models 66, 1 (2004), 1-23. 3. – reference: Yu Y., Zhou K., Xu D., Shi X., Bao H., Guo B., Shum H.-Y.: Mesh editing with poisson-based gradient field manipulation. ACM Trans. Graph. 23, 3 (2004), 644-651. 2, 6. – reference: Sumner R. W., Zwicker M., Gotsman C., Popovic′ J.: Mesh-based inverse kinematics. ACM Trans. Graph. 24, 3 (2005), 488-495. 2. – reference: Chu H.-K., Lee T.-Y.: Multiresolution mean shift clustering algorithm for shape interpolation. IEEE Trans. Vis. Comp. Graph. 15 (2009), 853-866. 2. – reference: Hasler N., Stoll C., Sunkel M., Rosenhahn B., Seidel H.-P.: A statistical model of human pose and body shape. Comput. Graph. Forum 28, 2 (2009), 337-346. 3. – reference: LaValle S. M.: Planning Algorithms. Cambridge University Press, 2006. 3. – reference: Martin S., Thomaszewski B., Grinspun E., Gross M. H.: Example-based elastic materials. ACM Trans. Graph. 30, 4 (2011), 72:1-8. 2. – reference: Hu J., Liu L., Wang G.: Dual Laplacian morphing for triangular meshes. Comput. Animat. Virtual Worlds 18, 4-5 (2007), 271-277. 2. – reference: Alexa M.: Differential coordinates for local mesh morphing and deformation. The Visual Computer 19, 2-3 (2003), 105-114. 2. – reference: Fröhlich S., Botsch M.: Example-driven deformations based on discrete shells. Comput. Graph. Forum 30, 8 (2011), 2246-2257. 2. – reference: Schreiner J., Asirvatham A., Praun E., Hoppe H.: Inter-surface mapping. ACM Trans. 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| SubjectTerms | 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 |
| Title | A Data-Driven Approach to Realistic Shape Morphing |
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