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Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild Shangzhe Wu Christian Rupprecht Andrea Vedaldi V ISUAL G EOMETRY G ROUP , U NIVERSITY OF O XFORD Agenda v Problem Introduction v Method Overview v Results

  1. Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild Shangzhe Wu Christian Rupprecht Andrea Vedaldi V ISUAL G EOMETRY G ROUP , U NIVERSITY OF O XFORD

  2. Agenda v Problem Introduction v Method Overview v Results v Discussions v Conclusions 29

  3. What is 3D Reconstruction? Vision Reconstruction Rendering Graphics 2D Observations 3D Representation 30

  4. Multi-view 3D Reconstruction Building Rome in a Day. Agarwal et al. ICCV’09 static scene but.. the world is dynamic 33

  5. Multi-view 3D Reconstruction Building Rome in a Day. The Relightables: Volumetric Performance Capture of Humans Agarwal et al. ICCV’09 with Realistic Relighting. Guo et al. SIGGRAPH Asia’19 static scene 100 cameras too expensive for me :( 34

  6. Learning-based Single-view 3D Reconstruction Neural Network Need supervision ! 3D prior learned during training 35

  7. Supervision during Training 3D ground truth or multi-views depth maps shape models silhouettes keypoints camera viewpoint 36

  8. Unsupervised Learning of 3D Objects 3D ground truth or multi-views depth maps shape models silhouettes keypoints camera viewpoint 37

  9. Unsupervised Learning of 3D Objects Training Data Output Unsup3D single-view images of a category instance-specific 3D shapes NO other supervision! 38

  10. input reconstruction input reconstruction 39

  11. Unsupervised Learning of 3D Objects Training Data Output Unsup3D single-view images of a category instance-specific 3D shapes NO other supervision! 40

  12. Symmetries in the World 41

  13. Training Pipeline: Photo-Geometric Autoencoding 42

  14. Photo-Geometric Autoencoding input # encoder encoder encoder decoder decoder view ! depth " texture Reconstruction Loss Renderer reconstruction $ # 43

  15. Photo-Geometric Autoencoding Q1 : How to avoid degenerate solutions? input # encoder encoder encoder decoder decoder view ! depth " texture Reconstruction Loss Renderer reconstruction $ # 44

  16. Photo-Geometric Autoencoding Q1 : How to avoid degenerate solutions? A1 : Enforce symmetry input # encoder encoder encoder decoder decoder view ! depth " texture Reconstruction Loss Renderer reconstruction $ # 45

  17. Photo-Geometric Autoencoding Q1 : How to avoid degenerate solutions? A1 : Enforce symmetry by flipping : horizontal flip input # encoder encoder encoder decoder decoder ? ? view ! depth " depth "′ texture flipped 46

  18. Photo-Geometric Autoencoding Q1 : How to avoid degenerate solutions? A1 : Enforce symmetry by flipping : horizontal flip input # encoder encoder encoder decoder decoder ? ? view ! depth " depth "′ texture flipped flip switch Reconstruction Loss ? Renderer reconstruction $ # 47

  19. Photo-Geometric Autoencoding Q1 : How to avoid degenerate solutions? A1 : Enforce symmetry by flipping : horizontal flip input # encoder encoder encoder decoder decoder ? ? view ! depth " depth "′ texture flipped flip switch Reconstruction Loss Renderer reconstruction $ # 48

  20. Photo-Geometric Autoencoding Q1 : How to avoid degenerate solutions? A1 : Enforce symmetry by flipping : horizontal flip input # encoder encoder encoder decoder decoder ? ? view ! depth " depth "′ texture flipped flip switch Reconstruction Loss ? Renderer reconstruction $ # 49

  21. Photo-Geometric Autoencoding Q1 : How to avoid degenerate solutions? A1 : Enforce symmetry by flipping : horizontal flip input # encoder encoder encoder decoder decoder view ! depth " depth "′ texture flipped flip switch Reconstruction Loss Renderer reconstruction $ # 50

  22. Photo-Geometric Autoencoding Q2 : What about non-symmetric lighting? : horizontal flip input # encoder encoder encoder decoder decoder view ! depth " depth "′ texture flipped 51

  23. Photo-Geometric Autoencoding Q2 : What about non-symmetric lighting? A2 : Enforce symmetry on albedo : horizontal flip input # encoder encoder encoder encoder decoder decoder view ! depth " depth "′ light ' albedo ( albedo (′ flip switch Reconstruction Loss shading Renderer reconstruction $ canonical view & # 52

  24. Photo-Geometric Autoencoding Q3 : Non-symmetric albedo, deformation, etc? : horizontal flip input # encoder encoder encoder encoder decoder decoder view ! depth " depth "′ light ' albedo ( albedo (′ flip switch Reconstruction Loss shading Renderer reconstruction $ canonical view & # 53

  25. Photo-Geometric Autoencoding Q3 : Non-symmetric albedo, deformation, etc? A3 : Predict uncertainty : horizontal flip input # encoder encoder encoder encoder encoder decoder decoder decoder conf. ) conf. )′ view ! depth " depth "′ light ' albedo ( albedo (′ flip switch Reconstruction Loss shading Renderer reconstruction $ canonical view & # 54

  26. Photo-Geometric Autoencoding Q3 : Non-symmetric albedo, deformation, etc? A3 : Predict uncertainty : horizontal flip input # encoder encoder encoder encoder encoder decoder decoder decoder conf. ) conf. )′ view ! depth " depth "′ light ' albedo ( albedo (′ flip switch Reconstruction Loss shading Renderer reconstruction $ canonical view & # 55

  27. Photo-Geometric Autoencoding Q3 : Non-symmetric albedo, deformation, etc? A3 : Predict uncertainty : horizontal flip input # encoder encoder encoder encoder encoder decoder decoder decoder conf. ) conf. )′ view ! depth " depth "′ light ' albedo ( albedo (′ flip switch Reconstruction Loss shading Renderer reconstruction $ canonical view & # 56

  28. Photo-Geometric Autoencoding Q3 : Non-symmetric albedo, deformation, etc? A3 : Predict uncertainty : horizontal flip input # encoder encoder encoder encoder encoder decoder decoder decoder conf. ) conf. )′ view ! depth " depth "′ light ' albedo ( albedo (′ flip switch Reconstruction Loss shading Renderer reconstruction $ canonical view & # 57

  29. Photo-Geometric Autoencoding Q3 : Non-symmetric albedo, deformation, etc? A3 : Predict uncertainty : horizontal flip input # encoder encoder encoder encoder encoder decoder decoder decoder conf. ) conf. )′ view ! depth " depth "′ light ' albedo ( albedo (′ flip switch Reconstruction Loss shading Renderer reconstruction $ canonical view & # 58

  30. Photo-Geometric Autoencoding Q3 : Non-symmetric albedo, deformation, etc? A3 : Predict uncertainty : horizontal flip input # encoder encoder encoder encoder encoder decoder decoder decoder conf. ) conf. )′ view ! depth " depth "′ light ' albedo ( albedo (′ flip switch Reconstruction Loss shading Renderer reconstruction $ canonical view & # 59

  31. Photo-Geometric Autoencoding Q3 : Non-symmetric albedo, deformation, etc? A3 : Predict uncertainty : horizontal flip input # encoder encoder encoder encoder encoder decoder decoder decoder conf. ) conf. )′ view ! depth " depth "′ light ' albedo ( albedo (′ flip switch Reconstruction Loss shading Renderer reconstruction $ canonical view & # 60

  32. Results on human faces Images taken from CelebA, 3DFAW 61

  33. input reconstruction input reconstruction 62

  34. Results on face paintings Images taken from [1] 63 [1] Elliot J. Crowley, Omkar M. Parkhi, and Andrew Zisserman. Face painting: querying art with photos. In Proc. BMVC, 2015.

  35. input reconstruction input reconstruction 64

  36. Results on abstract faces Images taken from [1] and the Internet 65 [1] Elliot J. Crowley, Omkar M. Parkhi, and Andrew Zisserman. Face painting: querying art with photos. In Proc. BMVC, 2015.

  37. input reconstruction input reconstruction 66

  38. Results on video frames Video clips taken from VoxCeleb2 We do not use videos for training or fine-tuning. These results are obtained by applying our model trained on CelebA frame by frame . 67

  39. 68 recon. new view rotated recon. new view rotated input input recon. new view rotated recon. new view rotated input input

  40. Relighting effects Images taken from CelebA 69

  41. input reconstruction input reconstruction 70

  42. Results on cat faces Images taken from [2] and [3] [2] Weiwei Zhang, Jian Sun, and Xiaoou Tang. Cat head detection - how to effectively exploit shape and texture features. In Proc. ECCV, 2008. [3] Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar. Cats and dogs. In Proc. CVPR, 2012. 71

  43. input reconstruction input reconstruction 72

  44. Results on synthetic cars Images rendered using ShapeNet 73

  45. input reconstruction input reconstruction 74

  46. Symmetry Plane Visualization 75

  47. Asymmetry Visualization 76

  48. Discussion: Ablation Studies 77

  49. Ablation – Symmetry full Input w/o albedo flip w/o depth flip Depth Normal Shading Albedo Shaded Recon. Insight #1 : Symmetry avoids degeneracy 78

  50. Ablation – Lighting (Shape from Shading) full Input w/o lighting Depth Normal Shading Albedo Shaded Recon. Insight #2 : Lighting avoids bumpy shapes and provides cues for shape 79

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