Style-Content Separation by Anisotropic Part Scales

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. Background • Motivation: Enrich a set of 3D models

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

4. Background • How to create new shapes? Style transfer ? Part proportion style

5. Background • How to create new shapes? Style transfer Style ?

6. Background • Style transfer is difficult: • Unsupervised • Correspondence is difficult to compute! • Geometry • Part proportion Significant shape variations!

7. Background • To address geometric variations: • Work at part level

8. Background • To address the part proportion variations: • Separate “style” from “content” Style 1 Style 2 Style 3

9. Back to our motivation… • Fill in the table:

10. Style-Content Separation • Fundamental to human perception

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

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

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

14. Style-Content Separation • Previous works: • Prerequisite: data correspondence • Dealt with independently • Correspondence itself is challenging!

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

16. Style clustering Co-segmentation Inter-style part correspondence Content classification Algorithm Overview • Pipeline

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

18. ? ? Style Distance • Issues: • Unknown segmentation: • Unknown correspondence:

19. Style Distance • 2D illustration of style distance …… ……

20. Style Distance • 2D illustration of style distance …… ……

21. Anisotropic Part Scales Style • Correspondence-free style signature Use Laplacian graph spectra: Binary relations: difference of part scales between adjacent OBBs OBB graph

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

23. Style Clustering • Spectral clustering

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

25. 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. Style clustering Co-segmentation Inter-style part correspondence Content classification Pipeline

27. 1D-to-1D 2D-to-3D 1D-to-2D 2D-to-2D Inter-Style Part Correspondence • Approach: Deform-to-fit • “Deformation driven shape correspondence” [Zhang et al. 2008] • Possible OBB-to-OBB transformations

28. Inter-Style Part Correspondence • Approach: Deform-to-fit Pruned priority-driven search

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

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

31. Synthesis by Style Transfer • OBB: scaling • Underlying geometry: space deformation content style

32. Results Hammers

33. Results Goblets

34. Results Humanoid

35. [Funkhouser et. al. 2004] 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 Defining and analyzing of more shape styles!

36. 감사합니다! Thank you! 谢谢 תודה