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

2/36 Background Motivation: Enrich a set of 3D models

3/36 Background How to create new shapes? Geometric (content) difference Part proportion (style) difference

4/36 ? Background How to create new shapes? Style transfer Part proportion style

5/36 ? Background How to create new shapes? Style transfer Style

6/36 Background Style transfer is difficult: –Unsupervised –Correspondence is difficult to compute! Geometry Part proportion Significant shape variations!

7/36 Background To address geometric variations: –Work at part level

8/36 Background To address the part proportion variations: –Separate “style” from “content” Style 1 Style 2 Style 3

9/36 Back to our motivation… Fill in the table:

10/36 Style-Content Separation Fundamental to human perception ContentStyle LanguageWordsAccents TextLettersFonts Human faceIdentitiesExpressions

11/36 Style-Content Separation Previous works: [Tanenbaum and Freeman 2000]Parameterized model

12/36 Style-Content Separation Previous works: “Morphable model” [Blatz and Vetter 1999] Statistical modeling

13/36 Style-Content Separation Previous works: “Style machines” [Brand and Hertzmann 2000] Statistical modeling

14/36 Style-Content Separation Previous works: –Prerequisite: data correspondence –Dealt with independently –Correspondence itself is challenging!

15/36 Style-Content Separation Our style: – Anisotropic Part Scales Our method: – Apply style-content separation in the correspondence stage!

16/36 Algorithm Overview Pipeline Style clustering Co-segmentation Inter-style part correspondence Content classification

17/36 Anisotropic Part Scales Style Idea: –Measure style distance between two shapes Compute style signature …… Part OBB Graph of given segmentation Euclidean Distance

18/36 Style Distance Issues: –Unknown segmentation: –Unknown correspondence: ? ?

19/36 Style Distance 2D illustration of style distance ……

20/36 Style Distance 2D illustration of style distance ……

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

22/36 Anisotropic Part Scales Style Style signature (correspondence free) Unitary characteristics: anisotropy OBB graph linear planar spherical Encode in graph Laplacian:

23/36 Style Clustering Spectral clustering

24/36 Pipeline Style clustering Co-segmentation Inter-style part correspondence Content classification

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

26/36 Pipeline Style clustering Co-segmentation Inter-style part correspondence Content classification

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

28/36 Inter-Style Part Correspondence Approach: Deform-to-fit Pruned priority-driven search

29/36 Pipeline Style clustering Co-segmentation Inter-style part correspondence Content classification

30/36 Content Classification Approach: –Light Field Descriptor [Chen et al. 2003] We do: part-wise comparison Part-level LFDGlobal LFD

31/36 Synthesis by Style Transfer OBB: scaling Underlying geometry: space deformation content style

32/36 Results Hammers

33/36 Results Goblets

34/36 Results Humanoid

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!

36/36 Thank you! 감사합니다 ! 谢谢 תודה