SpringerLink
Log in

Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13677))

Included in the following conference series:

Abstract

Over the years, 2D GANs have achieved great successes in photorealistic portrait generation. However, they lack 3D understanding in the generation process, thus they suffer from multi-view inconsistency problem. To alleviate the issue, many 3D-aware GANs have been proposed and shown notable results, but 3D GANs struggle with editing semantic attributes. The controllability and interpretability of 3D GANs have not been much explored. In this work, we propose two solutions to overcome these weaknesses of 2D GANs and 3D-aware GANs. We first introduce a novel 3D-aware GAN, SURF-GAN, which is capable of discovering semantic attributes during training and controlling them in an unsupervised manner. After that, we inject the prior of SURF-GAN into StyleGAN to obtain a high-fidelity 3D-controllable generator. Unlike existing latent-based methods allowing implicit pose control, the proposed 3D-controllable StyleGAN enables explicit pose control over portrait generation. This distillation allows direct compatibility between 3D control and many StyleGAN-based techniques (e.g., inversion and stylization), and also brings an advantage in terms of computational resources. Our codes are available at https://github.com/jgkwak95/SURF-GAN.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (Brazil)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (Brazil)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (Brazil)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abdal, R., Qin, Y., Wonka, P.: Image2StyleGAN: how to embed images into the StyleGAN latent space? In: Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  2. Abdal, R., Qin, Y., Wonka, P.: Image2StyleGAN++: how to edit the embedded images? In: Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  3. Abdal, R., Zhu, P., Mitra, N.J., Wonka, P.: StyleFlow: Attribute-conditioned exploration of StyleGAN-generated images using conditional continuous normalizing flows. ACM Trans. Graph. 40(3), 1–21 (2021)

    Article  Google Scholar 

  4. Alaluf, Y., Patashnik, O., Cohen-Or, D.: ReStyle: a residual-based StyleGAN encoder via iterative refinement. In: International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

  5. Alaluf, Y., Tov, O., Mokady, R., Gal, R., Bermano, A.H.: HyperStyle: StyleGAN inversion with hypernetworks for real image editing (2021)

    Google Scholar 

  6. Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: International Conference on Learning Representations (ICLR) (2019)

    Google Scholar 

  7. Chan, E.R., et al.: Efficient geometry-aware 3D generative adversarial networks. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

    Google Scholar 

  8. Chan, E.R., Monteiro, M., Kellnhofer, P., Wu, J., Wetzstein, G.: Pi-GAN: periodic implicit generative adversarial networks for 3D-aware image synthesis. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Google Scholar 

  9. Chen, A., Liu, R., **e, L., Chen, Z., Su, H., Yu, J.: SofGAN: a portrait image generator with dynamic styling. ACM Trans. Graph. 41(1), 1–26 (2022)

    Google Scholar 

  10. Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems (NeurIPS) (2016)

    Google Scholar 

  11. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  12. Deng, Y., Yang, J., Chen, D., Wen, F., Tong, X.: Disentangled and controllable face image generation via 3D imitative-contrastive learning. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  13. Deng, Y., Yang, J., **ang, J., Tong, X.: GRAM: generative radiance manifolds for 3D-aware image generation. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

    Google Scholar 

  14. Gadelha, M., Maji, S., Wang, R.: 3D shape induction from 2D views of multiple objects. In: International Conference on 3D Vision (3DV) (2017)

    Google Scholar 

  15. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (NeurIPS) (2014)

    Google Scholar 

  16. Gu, J., Liu, L., Wang, P., Theobalt, C.: StyleNeRF: a style-based 3D-aware generator for high-resolution image synthesis (2021)

    Google Scholar 

  17. Härkönen, E., Hertzmann, A., Lehtinen, J., Paris, S.: GANSpace: discovering interpretable GAN controls (2020)

    Google Scholar 

  18. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  19. He, Z., Kan, M., Shan, S.: EigenGAN: layer-wise eigen-learning for GANs. In: International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

  20. Henzler, P., Mitra, N.J., Ritschel, T.: Esca** Plato’s cave: 3D shape from adversarial rendering. In: International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  21. Hu, Y., Wu, X., Yu, B., He, R., Sun, Z.: Pose-guided photorealistic face rotation. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  22. Jeon, I., Lee, W., Pyeon, M., Kim, G.: IB-GAN: disengangled representation learning with information bottleneck generative adversarial networks. In: AAAI Conference on Artificial Intelligence (AAAI) (2021)

    Google Scholar 

  23. Kajiya, J.T., Von Herzen, B.P.: Ray tracing volume densities. SIGGRAPH (1984)

    Google Scholar 

  24. Kaneko, T., Hiramatsu, K., Kashino, K.: Generative attribute controller with conditional filtered generative adversarial networks. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  25. Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: International Conference on Learning Representations (ICLR) (2018)

    Google Scholar 

  26. Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)

    Google Scholar 

  27. Karras, T., et al.: Alias-free generative adversarial networks. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)

    Google Scholar 

  28. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  29. Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  30. Kowalski, M., Garbin, S.J., Estellers, V., Baltrušaitis, T., Johnson, M., Shotton, J.: CONFIG: controllable neural face image generation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 299–315. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_18

    Chapter  Google Scholar 

  31. Kwak, J.G., Li, Y., Yoon, D., Han, D., Ko, H.: Generate and edit your own character in a canonical view (2022)

    Google Scholar 

  32. Lee, W., Kim, D., Hong, S., Lee, H.: High-fidelity synthesis with disentangled representation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12371, pp. 157–174. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58574-7_10

    Chapter  Google Scholar 

  33. Liao, Y., Schwarz, K., Mescheder, L., Geiger, A.: Towards unsupervised learning of generative models for 3D controllable image synthesis. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  34. Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

  35. Lunz, S., Li, Y., Fitzgibbon, A., Kushman, N.: Inverse graphics GAN: learning to generate 3D shapes from unstructured 2D data (2020)

    Google Scholar 

  36. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: European Conference on Computer Vision (ECCV) (2020)

    Google Scholar 

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

    Google Scholar 

  38. Nguyen-Phuoc, T., Richardt, C., Mai, L., Yang, Y.L., Mitra, N.: BlockGAN: learning 3D object-aware scene representations from unlabelled images. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)

    Google Scholar 

  39. Niemeyer, M., Geiger, A.: GIRAFFE: representing scenes as compositional generative neural feature fields. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Google Scholar 

  40. Nitzan, Y., Gal, R., Brenner, O., Cohen-Or, D.: LARGE: latent-based regression through GAN semantics (2021)

    Google Scholar 

  41. Or-El, R., Luo, X., Shan, M., Shechtman, E., Park, J.J., Kemelmacher-Shlizerman, I.: StyleSDF: high-resolution 3D-consistent image and geometry generation. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

    Google Scholar 

  42. Pan, X., Dai, B., Liu, Z., Loy, C.C., Luo, P.: Do 2D GANs know 3D shape? Unsupervised 3D shape reconstruction from 2D image GANs. In: International Conference on Learning Representations (ICLR) (2021)

    Google Scholar 

  43. Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., Lischinski, D.: StyleCLIP: text-driven manipulation of StyleGAN imagery. In: International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

  44. Perez, E., Strub, F., de Vries, H., Dumoulin, V., Courville, A.C.: FiLM: visual reasoning with a general conditioning layer. In: AAAI Conference on Artificial Intelligence (AAAI) (2018)

    Google Scholar 

  45. Pinkney, J.N., Adler, D.: Resolution dependent GAN interpolation for controllable image synthesis between domains (2020)

    Google Scholar 

  46. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2015)

    Google Scholar 

  47. Richardson, E., et al.: Encoding in style: a StyleGAN encoder for image-to-image translation. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Google Scholar 

  48. Roich, D., Mokady, R., Bermano, A.H., Cohen-Or, D.: Pivotal tuning for latent-based editing of real images (2021)

    Google Scholar 

  49. Schwarz, K., Liao, Y., Niemeyer, M., Geiger, A.: GRAF: generative radiance fields for 3D-aware image synthesis. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)

    Google Scholar 

  50. Shen, Y., Gu, J., Tang, X., Zhou, B.: Interpreting the latent space of GANs for semantic face editing. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  51. Shen, Y., Zhou, B.: Closed-form factorization of latent semantics in GANs. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Google Scholar 

  52. Shi, Y., Aggarwal, D., Jain, A.K.: Lifting 2D StyleGAN for 3D-aware face generation. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Google Scholar 

  53. Shoshan, A., Bhonker, N., Kviatkovsky, I., Medioni, G.: GAN-control: explicitly controllable GANs. In: International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

  54. Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)

    Google Scholar 

  55. Sun, J., et al.: FENeRF: face editing in neural radiance fields (2021)

    Google Scholar 

  56. Szabó, A., Meishvili, G., Favaro, P.: Unsupervised generative 3D shape learning from natural images (2019)

    Google Scholar 

  57. Tewari, A., et al.: PIE: portrait image embedding for semantic control. ACM Trans. Graph. 39(6), 1–14 (2020)

    Article  Google Scholar 

  58. Tewari, A., et al.: StyleRig: rigging StyleGAN for 3D control over portrait images. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  59. Tov, O., Alaluf, Y., Nitzan, Y., Patashnik, O., Cohen-Or, D.: Designing an encoder for StyleGAN image manipulation. ACM Trans. Graph. 40(4), 1–14 (2021)

    Article  Google Scholar 

  60. Xue, Y., Li, Y., Singh, K.K., Lee, Y.J.: GIRAFFE HD: a high-resolution 3D-aware generative model. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

    Google Scholar 

  61. Yao, X., Newson, A., Gousseau, Y., Hellier, P.: A latent transformer for disentangled face editing in images and videos. In: International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

  62. Yin, X., Yu, X., Sohn, K., Liu, X., Chandraker, M.: Towards large-pose face frontalization in the wild. In: International Conference on Computer Vision (ICCV) (2017)

    Google Scholar 

  63. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  64. Zhang, Y., et al.: Image GANs meet differentiable rendering for inverse graphics and interpretable 3D neural rendering (2020)

    Google Scholar 

  65. Zhou, H., Liu, J., Liu, Z., Liu, Y., Wang, X.: Rotate-and-render: unsupervised photorealistic face rotation from single-view images. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  66. Zhou, P., **e, L., Ni, B., Tian, Q.: CIPS-3D: a 3D-aware generator of GANs based on conditionally-independent pixel synthesis (2021)

    Google Scholar 

  67. Zhu, J., Shen, Y., Zhao, D., Zhou, B.: In-domain GAN inversion for real image editing. In: European Conference on Computer Vision (ECCV) (2020)

    Google Scholar 

  68. Zhu, J.Y., et al.: Visual object networks: image generation with disentangled 3D representations. In: Advances in Neural Information Processing Systems (NeurIPS) (2018)

    Google Scholar 

  69. Zhu, X., Liu, X., Lei, Z., Li, S.Z.: Face alignment in full pose range: a 3D total solution. Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2017)

    Google Scholar 

Download references

Acknowledgement

This work was supported by DMLab. We also thank to Anonymous ECCV Reviewers for their constructive suggestions and discussions on our paper.

Author information

Authors and Affiliations

  1. Korea University, Seoul, South Korea

    Jeong-gi Kwak, Yuanming Li, Dongsik Yoon, Donghyeon Kim & Hanseok Ko

  2. Drexel Univiersity, Philadelphia, USA

    David Han

Authors
  1. Jeong-gi Kwak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuanming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dongsik Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Donghyeon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hanseok Ko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeong-gi Kwak .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 12113 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kwak, Jg., Li, Y., Yoon, D., Kim, D., Han, D., Ko, H. (2022). Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13677. Springer, Cham. https://doi.org/10.1007/978-3-031-19790-1_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19790-1_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19789-5

  • Online ISBN: 978-3-031-19790-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation