“Semantic shape editing using deformation handles”

  • ©Mehmet Ersin Yumer, Siddhartha Chaudhuri, Jessica K. Hodgins, and Levent Burak Kara

Conference:


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Title:

    Semantic shape editing using deformation handles

Session/Category Title: Shape Analysis


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Abstract:


    We propose a shape editing method where the user creates geometric deformations using a set of semantic attributes, thus avoiding the need for detailed geometric manipulations. In contrast to prior work, we focus on continuous deformations instead of discrete part substitutions. Our method provides a platform for quick design explorations and allows non-experts to produce semantically guided shape variations that are otherwise difficult to attain. We crowdsource a large set of pairwise comparisons between the semantic attributes and geometry and use this data to learn a continuous mapping from the semantic attributes to geometry. The resulting map enables simple and intuitive shape manipulations based solely on the learned attributes. We demonstrate our method on large datasets using two different user interaction modes and evaluate its usability with a set of user studies.

References:


    1. Allen, B., Curless, B., and Popović, Z. 2003. The space of human body shapes: reconstruction and parameterization from range scans. In ACM Trans. Graph., vol. 22, 587–594. Google ScholarDigital Library
    2. Averkiou, M., Kim, V. G., Zheng, Y., and Mitra, N. J. 2014. Shapesynth: Parameterizing model collections for coupled shape exploration and synthesis. In CGF, vol. 33(2), 125–134. Google ScholarDigital Library
    3. Blanz, V., and Vetter, T. 1999. A morphable model for the synthesis of 3d faces. In ACM SIGGRAPH, 187–194. Google ScholarDigital Library
    4. Bokeloh, M., Wand, M., Koltun, V., and Seidel, H.-P. 2011. Pattern-aware shape deformation using sliding dockers. In ACM Trans. Graph., vol. 30, 123. Google ScholarDigital Library
    5. Botsch, M., and Kobbelt, L. 2004. An intuitive framework for realtime modeling. ACM Trans. Graph. 23, 3, 630–634. Google ScholarDigital Library
    6. Botsch, M., and Sorkine, O. 2008. On linear variational surface deformation methods. IEEE TVCG 14, 1, 213–230. Google ScholarDigital Library
    7. Chaudhuri, S., Kalogerakis, E., Giguere, S., and Funkhouser, T. 2013. AttribIt: Content creation with semantic attributes. In ACM UIST, 193–202. Google ScholarDigital Library
    8. Cortes, C., and Vapnik, V. 1995. Support-vector networks. Machine Learning 20, 3, 273–297. Google ScholarDigital Library
    9. Deng, Z., and Neumann, U. 2008. Data-Driven 3D Facial Animation. Springer. Google ScholarDigital Library
    10. Freund, Y., Iyer, R., Schapire, R. E., and Singer, Y. 2003. An efficient boosting algorithm for combining p. The Journal of Machine Learning Research 4, 933–969. Google ScholarDigital Library
    11. Gal, R., Sorkine, O., Mitra, N. J., and Cohen-Or, D. 2009. iWires: an analyze-and-edit approach to shape manipulation. In ACM Trans. Graph., vol. 28, 33. Google ScholarDigital Library
    12. Golovinskiy, A., and Funkhouser, T. 2009. Consistent segmentation of 3D models. Computers & Graphics 33, 3, 262–269. Google ScholarDigital Library
    13. Kalogerakis, E., Chaudhuri, S., Koller, D., and Koltun, V. 2012. A probabilistic model for component-based shape synthesis. ACM Trans. Graph. 31, 4, 55. Google ScholarDigital Library
    14. Kittur, A., Chi, E. H., and Suh, B. 2008. Crowdsourcing user studies with Mechanical Turk. In ACM SIGCHI, 453–456. Google ScholarDigital Library
    15. Kovashka, A., Parikh, D., and Grauman, K. 2012. Whittlesearch: Image search with relative attribute feedback. In IEEE CVPR, 2973–2980. Google ScholarDigital Library
    16. Kraevoy, V., Sheffer, A., Shamir, A., and Cohen-Or, D. 2008. Non-homogeneous resizing of complex models. In ACM Trans. Graph., vol. 27, 111. Google ScholarDigital Library
    17. Laffont, P.-Y., Ren, Z., Tao, X., Qian, C., and Hays, J. 2014. Transient attributes for high-level understanding and editing of outdoor scenes. ACM Trans. Graph. 33, 4, 149. Google ScholarDigital Library
    18. Lewis, J., Pighin, F., and Anjyo, K. 2010. Scattered data interpolation and approximation for computer graphics. In ACM SIGGRAPH ASIA Courses. Google ScholarDigital Library
    19. Marks, J., Andalman, B., et al. 1997. Design galleries: A general approach to setting parameters for computer graphics and animation. In ACM SIGGRAPH, 389–400. Google ScholarDigital Library
    20. Mitra, N. J., Wand, M., Zhang, H., Cohen-Or, D., and Bokeloh, M. 2013. Structure-aware shape processing. In Eurographics STARs, 175–197.Google Scholar
    21. O’Donovan, P., Lībeks, J., Agarwala, A., and Hertzmann, A. 2014. Exploratory font selection using crowdsourced attributes. ACM Trans. Graph. 33, 4, 92. Google ScholarDigital Library
    22. Orsborn, S., Cagan, J., and Boatwright, P. 2009. Quantifying aesthetic form preference in a utility function. Journal of Mechanical Design 131, 6, 061001.Google ScholarCross Ref
    23. Ovsjanikov, M., Li, W., Guibas, L., and Mitra, N. J. 2011. Exploration of continuous variability in collections of 3D shapes. ACM Trans. Graph. 30, 4, 33. Google ScholarDigital Library
    24. Parikh, D., and Grauman, K. 2011. Relative attributes. In IEEE Conference on Computer Vision, 503–510. Google ScholarDigital Library
    25. Roweis, S. T., and Saul, L. K. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326.Google ScholarCross Ref
    26. Schelling, T. C. 1980. The strategy of conflict. Harvard University Press.Google Scholar
    27. Shepard, D. 1968. A two-dimensional interpolation function for irregularly-spaced data. In ACM Nat. Conf., 517–524. Google ScholarDigital Library
    28. Sidi, O., van Kaick, O., Kleiman, Y., Zhang, H., and Cohen-Or, D. 2011. Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering. ACM Trans. Graph. 30, 6, 126. Google ScholarDigital Library
    29. Steel, R. G., Torrie, J. H., et al. 1960. Principles and procedures of statistics. Principles and procedures of statistics..Google Scholar
    30. Tao, L., Yuan, L., and Sun, J. 2009. Skyfinder: attribute-based sky image search. In ACM Trans. Graph., vol. 28, 68. Google ScholarDigital Library
    31. Thode, H. C. 2002. Testing for normality, vol. 164. CRC Press.Google Scholar
    32. van Kaick, O., Xu, K., Zhang, H., Wang, Y., Sun, S., Shamir, A., and Cohen-Or, D. 2013. Co-hierarchical analysis of shape structures. ACM Trans. Graph. 32, 4, 69. Google ScholarDigital Library
    33. Xu, K., Zhang, H., Cohen-Or, D., and Chen, B. 2012. Fit and diverse: Set evolution for inspiring 3D shape galleries. ACM Trans. Graph. 31, 4, 57. Google ScholarDigital Library
    34. Yuksel, S. E., Wilson, J. N., and Gader, P. D. 2012. Twenty years of mixture of experts. NNLS 23, 1177–1193.Google Scholar
    35. Yumer, M. E., and Kara, L. B. 2012. Co-abstraction of shape collections. ACM Trans. Graph. 31(6), 166:1–166:11. Google ScholarDigital Library
    36. Yumer, M. E., and Kara, L. B. 2014. Co-constrained handles for deformation in shape collections. ACM Trans. Graph. 33(6), 187:1–187:11. Google ScholarDigital Library
    37. Yumer, M. E., Chun, W., and Makadia, A. 2014. Co-segmentation of textured 3D shapes with sparse annotations. In IEEE CVPR, 240–247. Google ScholarDigital Library
    38. Zheng, Y., Fu, H., Cohen-Or, D., Au, O. K.-C., and Tai, C.-L. 2011. Component-wise controllers for structure-preserving shape manipulation. In CGF, vol. 30, 563–572.Google ScholarCross Ref


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