Model-Driven 3D Content Creation as Variation

1. NUDT TAU ZJU SFU Model-Driven 3D Content Creation as Variation Hao (Richard) Zhang – 张皓 GrUVi Lab, Simon Fraser University (SFU) Talk @ HKUST, 04/21/11

2. 3D content creation Inspiration? Inspiration  a readily usable digital 3D model

3. Realistic reconstruction Inspiration = real-world data [Nan et al., SIGGRAPH 2010]

4. Creative inspiration Creation of novel 3D shapes Inspiration = design concept, mental picture, … High demand in VFX, games, simulation, VR, … sketch

5. 3D content creation is hard 2D-to-3D: an ill-posed problem Shape from shading, sketch-based modeling, … Creation from scratch is hard: job for skilled artists One of the most central problems in graphics; One of the most discussed at SIG’10 panel

6. Usable 3D content even harder Models created are meant for subsequent use Creation of readily usable 3D models

7. Usable 3D content even harder Models created are meant for subsequent use Creation of readily usable 3D models Higher-level information beyond low-level mesh Part or segmentation information Structural relations between parts Correspondence to relevant models, etc. Hard shape analysis problems!

8. Key: model reuse Reuse existing 3D models and associated info Model-driven approach: creation is driven by or based on existing (pre-analyzed) models

9. Key: model reuse Reuse existing 3D models and associated info Model-driven approach: creation is driven by or based on existing (pre-analyzed) models Two primary modes of reuse: New creation via part composition

10. Key: model reuse Reuse existing 3D models and associated info Model-driven approach: creation is driven by or based on existing (pre-analyzed) models Two primary modes of reuse: New creation via part composition New creation as variationor modification of existing model(s), e.g.,a warp or a deformation

11. Modeling by example New models composed by parts retrieved from an existing data repository Key: retrieve relevant parts Many variants … [Funkhouser et al., SIGGRAPH 2004]

12. Pros and cons Pros: Significant deviation from existing models Exploratory modeling via part suggestions [Chaudhuri & Koltun., SIG Asia 2010]

13. Pros and cons Pros: Significant deviation from existing models Exploratory modeling with part suggestions Cons: Are models composed by parts readily usable?

14. Pros and cons Pros: Significant deviation from existing models Exploratory modeling with part suggestions Cons: Are models composed by parts readily usable?  structure lost by part composition; how to stitch?

15. Pros and cons Pros: Significant deviation from existing models Exploratory modeling with part suggestions Cons: Are models composed by parts readily usable?  structure lost by part composition; how to stitch? Does part exploration always reflect user design intent?

16. Model-driven creation as variation New creation as variationof existing model(s) Inspiration = a model set Inspiration = photographs Photo-inspired 3D model creation Enrich a set; generate “more of the same” …

17. Model-driven creation as variation New creation as variationof existing model(s) Enrich a set; generate “more of the same” … Inspiration = a model set

18. Style-Content Separation by Anisotropic Part Scales Kai Xu1,2, Honghua Li2, Hao Zhang2, Daniel Cohen-Or3 Yueshan Xiong2, and Zhi-Quan Cheng2 1Simon Fraser Universtiy 2National Univ. of Defense Tech. 3Tel-Aviv University

19. Motivation Enrich a set of 3D models with their derivatives Set belongs to the same family or class

20. Variations in shape parts in the set Geometric or content difference Part proportion (= style) difference

21. Style transfer as a derivative ? Part proportion style

22. Style transfer as a derivative Part proportion style ?

23. Difficulty with style transfer Style transfer needs part correspondence Part correspondence is difficult Unsupervised problem Both content and style variations Variations can be significant!

24. Work at part and OBB level Parts enclosed and characterized by tight oriented bounding boxes (OBBs)

25. Style content separation To address both shape variations in the set Separate treatment of “style” and “content” Content Style 1 Style Style 2 Style 3

26. Style transfer as a derivative Creation = filling in the style-content table

27. Style vs. content Fundamental to human perception

28. Style content separation Previous works on faces, motion, etc. Prerequisite: data correspondence Correspondence dealt with independently Correspondence itself is the very challenge!

29. Our approach • One particular style: • Anisotropic part scales or part proportions

30. Our approach • One particular style: • Anisotropic part scales or part proportions • The approach: • Style-content separation with style clustering inacorrespondence-free way

31. Algorithm overview Pipeline Style clustering Co-segmentation Inter-style part correspondence Content classification

32. Anisotropic part scales Measure style distance between two shapes

33. Anisotropic part scales Measure style distance between two shapes Part OBB graphs ofgiven segmentation

34. Anisotropic part scales Measure style distance between two shapes … … Part OBB graphs ofgiven segmentation Compute style signatures

35. Anisotropic part scales Measure style distance between two shapes … … Part OBB graphs ofgiven segmentation Compute style signatures Euclidean distance

36. Style distance issues Unknown segmentation Unknown correspondence ? ?

37. Style distance Search over all part compositions and part counts …… ……

38. Style distance For each part count, find minimal distance …… A good signature will return min distance across all part counts to reflect corresponding part decompositions … ……

39. Correspondence-free style signature UseLaplacian graph spectra: Binary relations: difference of part scales between adjacent OBBs OBB graph

40. Correspondence-free style signature Use Laplacian graph spectra: Unary attributes: anisotropy of parts Graph spectra is permutation-free spherical linear planar OBB graph

41. Style clustering Spectral clustering using style distances

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

43. Co-segmentation Approach: Consistent segmentation [Golovinskiy & Funkhouser, SMI 09] Initial guess: global alignment (ICP) [Golovinskiy & Funkhouser 09]

44. Co-segmentation Approach: Consistent segmentation [Golovinskiy & Funkhouser, SMI 09] Initial guess: global alignment (ICP) We co-segment within a style cluster Removing non-homogeneous part scaling from analysis [Golovinskiy & Funkhouser 09]

45. Co-segmentation Approach: Consistent segmentation [Golovinskiy & Funkhouser, SMI 09] Initial guess: global alignment (ICP) We co-segment within a style cluster Removing non-homogeneous part scaling from analysis [Golovinskiy & Funkhouser 09] After style separation

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

47. Inter-style part correspondence Approach: deform-to-fit Deformation-driven correspondence [Zhang et al., SGP 08] Consider common interactions between OBBs 1D-to-1D 2D-to-3D 1D-to-2D 2D-to-2D

48. Inter-style part correspondence Deform-to-fit: appropriate deformation energy Pruned priority-driven search

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

50. Content classification Use Light Field Descriptor [Chen et al. 2003] Compare corresponding parts Part-level LFD Global LFD