Skip to main content
SpringerLink
Log in

GeLaTO: Generative Latent Textured Objects

  • Conference paper
  • First Online:
Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12351))

Included in the following conference series:

Abstract

Accurate modeling of 3D objects exhibiting transparency, reflections and thin structures is an extremely challenging problem. Inspired by billboards and geometric proxies used in computer graphics, this paper proposes Generative Latent Textured Objects (GeLaTO), a compact representation that combines a set of coarse shape proxies defining low frequency geometry with learned neural textures, to encode both medium and fine scale geometry as well as view-dependent appearance. To generate the proxies’ textures, we learn a joint latent space allowing category-level appearance and geometry interpolation. The proxies are independently rasterized with their corresponding neural texture and composited using a U-Net, which generates an output photorealistic image including an alpha map. We demonstrate the effectiveness of our approach by reconstructing complex objects from a sparse set of views. We show results on a dataset of real images of eyeglasses frames, which are particularly challenging to reconstruct using classical methods. We also demonstrate that these coarse proxies can be handcrafted when the underlying object geometry is easy to model, like eyeglasses, or generated using a neural network for more complex categories, such as cars.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdal, R., Qin, Y., Wonka, P.: Image2StyleGAN: how to embed images into the StyleGAN latent space? In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 2019. https://doi.org/10.1109/iccv.2019.00453

  2. Aliev, K.A., Ulyanov, D., Lempitsky, V.: Neural point-based graphics (2019)

    Google Scholar 

  3. Autonomous Robotics and Perception Group: Calibu Camera Calibration Library. http://github.com/arpg/calibu

  4. Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 187–194 (1999)

    Google Scholar 

  5. Bojanowski, P., Joulin, A., Lopez-Pas, D., Szlam, A.: Optimizing the latent space of generative networks. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning, Proceedings of Machine Learning Research (2018)

    Google Scholar 

  6. Bregler, C., Hertzmann, A., Biermann, H.: Recovering non-rigid 3D shape from image streams. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000 (Cat. No. PR00662), vol. 2, pp. 690–696. IEEE (2000)

    Google Scholar 

  7. Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. Technical report, arXiv:1512.03012 [cs.GR], Stanford University – Princeton University – Toyota Technological Institute at Chicago (2015)

  8. Chen, A., et al.: Deep surface light fields. In: Proceedings of the ACM Computer Graphics Interactive Techniques, vol. 1, no. 1, July 2018

    Google Scholar 

  9. Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019

    Google Scholar 

  10. Décoret, X., Durand, F., Sillion, F.X., Dorsey, J.: Billboard clouds for extreme model simplification. ACM Trans. Graph. 22(3), 689–696 (2003). https://doi.org/10.1145/882262.882326

    Article  Google Scholar 

  11. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  12. Fuhrmann, A., Umlauf, E., Mantler, S.: Extreme model simplification for forest rendering, pp. 57–66, January 2005

    Google Scholar 

  13. Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1362–1376 (2010)

    Article  Google Scholar 

  14. Google: AR Core Augmented Faces. https://developers.google.com/ar/develop/ios/augmented-faces/overview

  15. Groueix, T., Fisher, M., Kim, V.G., Russell, B., Aubry, M.: AtlasNet: a papier-Mâché approach to learning 3D surface generation. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  16. Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017

    Google Scholar 

  17. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43

    Chapter  Google Scholar 

  18. Kanazawa, A., Tulsiani, S., Efros, A.A., Malik, J.: Learning category-specific mesh reconstruction from image collections. In: ECCV (2018)

    Google Scholar 

  19. Kar, A., Tulsiani, S., Carreira, J., Malik, J.: Category-specific object reconstruction from a single image. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015

    Google Scholar 

  20. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. https://doi.org/10.1109/cvpr.2019.00453

  21. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  22. Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)

  23. Kulkarni, N., Gupta, A., Tulsiani, S.: Canonical surface mapping via geometric cycle consistency. In: The IEEE International Conference on Computer Vision (ICCV), October 2019

    Google Scholar 

  24. Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2008)

    Article  Google Scholar 

  25. Liu, L., Chen, N., Ceylan, D., Theobalt, C., Wang, W., Mitra, N.J.: CurveFusion: reconstructing thin structures from RGBD sequences. ACM Trans. Graph. 37(6), 1–2 (2018)

    Google Scholar 

  26. Lombardi, S., Saragih, J., Simon, T., Sheikh, Y.: Deep appearance models for face rendering. ACM Trans. Graph. 37(4), 1–3 (2018)

    Article  Google Scholar 

  27. Lombardi, S., Simon, T., Saragih, J., Schwartz, G., Lehrmann, A., Sheikh, Y.: Neural volumes: learning dynamic renderable volumes from images. ACM Trans. Graph. 38(4), (2019). https://doi.org/10.1145/3306346.3323020

  28. Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3D reconstruction in function space. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  29. Meshry, M., et al.: Neural rerendering in the wild. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019

    Google Scholar 

  30. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis (2020)

    Google Scholar 

  31. Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., Yang, Y.L.: HoloGAN: unsupervised learning of 3D representations from natural images. In: The IEEE International Conference on Computer Vision (ICCV), October 2019

    Google Scholar 

  32. Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 165–174 (2019)

    Google Scholar 

  33. Pittaluga, F., Koppal, S.J., Bing Kang, S., Sinha, S.N.: Revealing scenes by inverting structure from motion reconstructions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 145–154 (2019)

    Google Scholar 

  34. Pollefeys, M., et al.: Visual modeling with a hand-held camera. Int. J. Comput. Vision 59(3), 207–232 (2004)

    Article  MathSciNet  Google Scholar 

  35. Porter, T., Duff, T.: Compositing digital images. SIGGRAPH Comput. Graph. 18(3), 253–259 (1984)

    Article  Google Scholar 

  36. Rohlf, J., Helman, J.: Iris performer: a high performance multiprocessing toolkit for real-time 3D graphics. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1994 (1994)

    Google Scholar 

  37. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  38. Shan, Q., Agarwal, S., Curless, B.: Refractive height fields from single and multiple images. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 286–293, June 2012

    Google Scholar 

  39. Sitzmann, V., Thies, J., Heide, F., Nießner, M., Wetzstein, G., Zollhöfer, M.: DeepVoxels: learning persistent 3D feature embeddings. In: Proceedings Computer Vision and Pattern Recognition (CVPR). IEEE (2019)

    Google Scholar 

  40. Sitzmann, V., Zollhöfer, M., Wetzstein, G.: Scene representation networks: continuous 3D-structure-aware neural scene representations. In: Advances in Neural Information Processing Systems, pp. 1119–1130 (2019)

    Google Scholar 

  41. Tewari, A., et al.: State of the art on neural rendering. In: Computer Graphics Forum (EG STAR 2020) (2020)

    Google Scholar 

  42. Thies, J., Zollhöfer, M., Theobalt, C., Stamminger, M., Nießner, M.: IGNOR: image-guided neural object rendering. arXiv 2018 (2018)

    Google Scholar 

  43. Thies, J., Zollhöfer, M., Nießner, M.: Deferred neural rendering: image synthesis using neural textures. ACM Trans. Graph. 38(4), 1–2 (2019)

    Article  Google Scholar 

  44. Tunwattanapong, B., et al.: Acquiring reflectance and shape from continuous spherical harmonic illumination. ACM Trans. Graph. 32(4) (2013)

    Google Scholar 

  45. Whelan, T., et al.: Reconstructing scenes with mirror and glass surfaces. ACM Trans. Graph. 37(4) (2018)

    Google Scholar 

  46. Zhang, Q., Guo, Y., Laffont, P., Martin, T., Gross, M.: A virtual try-on system for prescription eyeglasses. IEEE Comput. Graph. Appl. 37(4), 84–93 (2017). https://doi.org/10.1109/MCG.2017.3271458

    Article  Google Scholar 

  47. Zhang, R.: Making convolutional networks shift-invariant again. arXiv preprint arXiv:1904.11486 (2019)

Download references

Author information

Authors and Affiliations

  1. Google Research, Seattle, USA

    Ricardo Martin-Brualla, Matthew Brown & Dan B. Goldman

  2. Google Research, Mountain View, CA, USA

    Rohit Pandey & Sofien Bouaziz

Authors
  1. Ricardo Martin-Brualla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rohit Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sofien Bouaziz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matthew Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dan B. Goldman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ricardo Martin-Brualla .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 2 (mp4 87698 KB)

Supplementary material 1 (pdf 114 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Martin-Brualla, R., Pandey, R., Bouaziz, S., Brown, M., Goldman, D.B. (2020). GeLaTO: Generative Latent Textured Objects. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58539-6_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58538-9

  • Online ISBN: 978-3-030-58539-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation