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Published byMilton McCoy Modified over 9 years ago
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1 Style-Content Separation by Anisotropic Part Scales Kai Xu, Honghua Li, Hao Zhang, Daniel Cohen-Or Yueshan Xiong, Zhi-Quan Cheng Simon Fraser Universtiy National Univ. of Defense Tech. Tel-Aviv University
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2/36 Background Motivation: Enrich a set of 3D models
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3/36 Background How to create new shapes? Geometric (content) difference Part proportion (style) difference
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4/36 ? Background How to create new shapes? Style transfer Part proportion style
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5/36 ? Background How to create new shapes? Style transfer Style
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6/36 Background Style transfer is difficult: –Unsupervised –Correspondence is difficult to compute! Geometry Part proportion Significant shape variations!
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7/36 Background To address geometric variations: –Work at part level
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8/36 Background To address the part proportion variations: –Separate “style” from “content” Style 1 Style 2 Style 3
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9/36 Back to our motivation… Fill in the table:
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10/36 Style-Content Separation Fundamental to human perception ContentStyle LanguageWordsAccents TextLettersFonts Human faceIdentitiesExpressions
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11/36 Style-Content Separation Previous works: [Tanenbaum and Freeman 2000]Parameterized model
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12/36 Style-Content Separation Previous works: “Morphable model” [Blatz and Vetter 1999] Statistical modeling
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13/36 Style-Content Separation Previous works: “Style machines” [Brand and Hertzmann 2000] Statistical modeling
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14/36 Style-Content Separation Previous works: –Prerequisite: data correspondence –Dealt with independently –Correspondence itself is challenging!
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15/36 Style-Content Separation Our style: – Anisotropic Part Scales Our method: – Apply style-content separation in the correspondence stage!
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16/36 Algorithm Overview Pipeline Style clustering Co-segmentation Inter-style part correspondence Content classification
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17/36 Anisotropic Part Scales Style Idea: –Measure style distance between two shapes Compute style signature …… Part OBB Graph of given segmentation Euclidean Distance
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18/36 Style Distance Issues: –Unknown segmentation: –Unknown correspondence: ? ?
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19/36 Style Distance 2D illustration of style distance ……
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20/36 Style Distance 2D illustration of style distance ……
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21/36 Anisotropic Part Scales Style Correspondence-free style signature Binary relations: difference of part scales between adjacent OBBs Use Laplacian graph spectra: OBB graph
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22/36 Anisotropic Part Scales Style Style signature (correspondence free) Unitary characteristics: anisotropy OBB graph linear planar spherical Encode in graph Laplacian:
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23/36 Style Clustering Spectral clustering
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24/36 Pipeline Style clustering Co-segmentation Inter-style part correspondence Content classification
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25/36 Co-segmentation Approach: –“Consistent segmentation of 3D models” [Golovinskiy and Funkhouser 2009] –Initial guess: global alignment (ICP) We do: within a style cluster –No non-homogeneous part scaling issue! [Golovinskiy and Funkhouser 2009] Ours
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26/36 Pipeline Style clustering Co-segmentation Inter-style part correspondence Content classification
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27/36 Inter-Style Part Correspondence Approach: Deform-to-fit –“Deformation driven shape correspondence” [Zhang et al. 2008] –Possible OBB-to-OBB transformations 1D-to-1D 1D-to-2D2D-to-2D 2D-to-3D
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28/36 Inter-Style Part Correspondence Approach: Deform-to-fit Pruned priority-driven search
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29/36 Pipeline Style clustering Co-segmentation Inter-style part correspondence Content classification
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30/36 Content Classification Approach: –Light Field Descriptor [Chen et al. 2003] We do: part-wise comparison Part-level LFDGlobal LFD
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31/36 Synthesis by Style Transfer OBB: scaling Underlying geometry: space deformation content style
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32/36 Results Hammers
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33/36 Results Goblets
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34/36 Results Humanoid
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35/36 Limitations and Future Works Requirement on datasets: –Same semantic class –Sufficient variety in style –Initial (over) segmentation needs to be sufficiently meaningful Does not create new content Only deals with part anisotropic scales [Funkhouser et. al. 2004] Defining and analyzing of more shape styles!
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36/36 Thank you! 감사합니다 ! 谢谢 תודה
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