Skip to main content

3D-Aware Semantic-Guided Generative Model for Human Synthesis

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

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13675))

Included in the following conference series:

Abstract

Generative Neural Radiance Field (GNeRF) models, which extract implicit 3D representations from 2D images, have recently been shown to produce realistic images representing rigid/semi-rigid objects, such as human faces or cars. However, they usually struggle to generate high-quality images representing non-rigid objects, such as the human body, which is of a great interest for many computer graphics applications. This paper proposes a 3D-aware Semantic-Guided Generative Model (3D-SGAN) for human image synthesis, which combines a GNeRF with a texture generator. The former learns an implicit 3D representation of the human body and outputs a set of 2D semantic segmentation masks. The latter transforms these semantic masks into a real image, adding a realistic texture to the human appearance. Without requiring additional 3D information, our model can learn 3D human representations with a photo-realistic, controllable generation. Our experiments on the DeepFashion dataset show that 3D-SGAN significantly outperforms the most recent baselines. The code is available at https://github.com/zhangqianhui/3DSGAN.

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

Access this chapter

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 (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

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: ICCV (2019)

    Google Scholar 

  2. Abdal, R., Zhu, P., Mitra, N., Wonka, P.: Styleflow: attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. ACM TOG 40(3), 1–21 (2020)

    Article  Google Scholar 

  3. Alp Güler, R., Neverova, N., Kokkinos, I.: Densepose: dense human pose estimation in the wild. In: CVPR (2018)

    Google Scholar 

  4. Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE TPAMI 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  5. Balakrishnan, G., Zhao, A., Dalca, A.V., Durand, F., Guttag, J.: Synthesizing images of humans in unseen poses. In: CVPR (2018)

    Google Scholar 

  6. Brock, A., Donahue, J., Simonyan, K.: Large scale gan training for high fidelity natural image synthesis. In: ICLR (2019)

    Google Scholar 

  7. Chan, E., Monteiro, M., Kellnhofer, P., Wu, J., Wetzstein, G.: pi-gan: periodic implicit generative adversarial networks for 3d-aware image synthesis. In: CVPR (2021)

    Google Scholar 

  8. Chan, E.R., et al.: Efficient geometry-aware 3d generative adversarial networks. arXiv preprint. arXiv:2112.07945 (2021)

  9. Chen, X., Cohen-Or, D., Chen, B., Mitra, N.J.: Towards a neural graphics pipeline for controllable image generation. CGF 40(2), 127–140 (2021)

    Google Scholar 

  10. Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: CVPR (2018)

    Google Scholar 

  11. Deng, Y., Yang, J., Xiang, J., Tong, X.: Gram: generative radiance manifolds for 3d-aware image generation (2022)

    Google Scholar 

  12. DeVries, T., Bautista, M.A., Srivastava, N., Taylor, G.W., Susskind, J.M.: Unconstrained scene generation with locally conditioned radiance fields. In: ICCV (2021)

    Google Scholar 

  13. Gadelha, M., Maji, S., Wang, R.: 3d shape induction from 2d views of multiple objects. In: 3DV (2017)

    Google Scholar 

  14. Goodfellow, I., et al.: Generative adversarial nets. In: NeurIPS (2014)

    Google Scholar 

  15. Grigorev, A., Sevastopolsky, A., Vakhitov, A., Lempitsky, V.: Coordinate-based texture inpainting for pose-guided image generation. In: CVPR (2019)

    Google Scholar 

  16. Grigorev, A., et al.: Stylepeople: a generative model of fullbody human avatars. In: CVPR (2021)

    Google Scholar 

  17. Gu, J., Liu, L., Wang, P., Theobalt, C.: Stylenerf: a style-based 3d-aware generator for high-resolution image synthesis. ICLR (2022)

    Google Scholar 

  18. Güler, R.A., Neverova, N., Kokkinos, I.: Densepose: dense human pose estimation in the wild. In: CVPR (2018)

    Google Scholar 

  19. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of wasserstein gans. In: NeurIPS (2017)

    Google Scholar 

  20. He, Z., Kan, M., Shan, S.: Eigengan: layer-wise eigen-learning for gans. In: ICCV (2021)

    Google Scholar 

  21. Henderson, P., Ferrari, V.: Learning single-image 3d reconstruction by generative modelling of shape, pose and shading. International Journal of Computer Vision 128(4), 835–854 (2019). https://doi.org/10.1007/s11263-019-01219-8

    Article  MATH  Google Scholar 

  22. Henzler, P., Mitra, N.J., Ritschel, T.: Escaping plato’s cave: 3d shape from adversarial rendering. In: ICCV (2019)

    Google Scholar 

  23. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: NeurIPS (2017)

    Google Scholar 

  24. Huang, S., et al.: Generating person images with appearance-aware pose stylizer. In: IJCAI (2020)

    Google Scholar 

  25. Huang, X., Liu, M.Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: ECCV (2018)

    Google Scholar 

  26. Jain, A., Tancik, M., Abbeel, P.: Putting nerf on a diet: semantically consistent few-shot view synthesis. In: ICCV (2021)

    Google Scholar 

  27. Jinsong, Z., Kun, L., Yu-Kun, L., Jingyu, Y.: PISE: person image synthesis and editing with decoupled gan. In: CVPR (2021)

    Google Scholar 

  28. Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation. In: ICLR (2018)

    Google Scholar 

  29. Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. In: NeurIPS (2020)

    Google Scholar 

  30. Karras, T., et al.: Alias-free generative adversarial networks. In: NeurIPS (2021)

    Google Scholar 

  31. Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: CVPR (2020)

    Google Scholar 

  32. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR (2019)

    Google Scholar 

  33. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)

    Google Scholar 

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

  35. Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: ICLR (2013)

    Google Scholar 

  36. Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. In: ICML (2016)

    Google Scholar 

  37. Lassner, C., Pons-Moll, G., Gehler, P.V.: A generative model of people in clothing. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 853–862 (2017)

    Google Scholar 

  38. Liao, Y., Schwarz, K., Mescheder, L., Geiger, A.: Towards unsupervised learning of generative models for 3d controllable image synthesis. In: CVPR (2020)

    Google Scholar 

  39. Liu, W., Piao, Z., Tu, Z., Luo, W., Ma, L., Gao, S.: Liquid warping gan with attention: a unified framework for human image synthesis. IEEE TPAMI 44(9), 5114–5132 (2021)

    Google Scholar 

  40. Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X.: Deepfashion: powering robust clothes recognition and retrieval with rich annotations. In: CVPR (2016)

    Google Scholar 

  41. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM TOG 34(6), 1–16 (2015)

    Article  Google Scholar 

  42. Lunz, S., Li, Y., Fitzgibbon, A.W., Kushman, N.: Inverse graphics GAN: learning to generate 3d shapes from unstructured 2d data. CoRR abs/2002.12674 (2020). https://arxiv.org/abs/2002.12674

  43. Lv, Z., Li, X., Li, X., Li, F., Lin, T., He, D., Zuo, W.: Learning semantic person image generation by region-adaptive normalization. In: CVPR (2021)

    Google Scholar 

  44. Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., Van Gool, L.: Pose guided person image generation. In: NeurIPS (2017)

    Google Scholar 

  45. Ma, L., Sun, Q., Georgoulis, S., Van Gool, L., Schiele, B., Fritz, M.: Disentangled person image generation. In: CVPR (2018)

    Google Scholar 

  46. Men, Y., Mao, Y., Jiang, Y., Ma, W.Y., Lian, Z.: Controllable person image synthesis with attribute-decomposed gan. In: CVPR (2020)

    Google Scholar 

  47. 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: ECCV (2020)

    Google Scholar 

  48. Neverova, N., Alp Guler, R., Kokkinos, I.: Dense pose transfer. In: ECCV (2018)

    Google Scholar 

  49. Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., Yang, Y.L.: Hologan: unsupervised learning of 3d representations from natural images. In: ICCV (2019)

    Google Scholar 

  50. Nguyen-Phuoc, T., Richardt, C., Mai, L., Yang, Y.L., Mitra, N.: Blockgan: learning 3d object-aware scene representations from unlabelled images. In: NeurIPS (2020)

    Google Scholar 

  51. Niemeyer, M., Geiger, A.: CAMPARI: camera-aware decomposed generative neural radiance fields. In: 3DV (2021)

    Google Scholar 

  52. Niemeyer, M., Geiger, A.: GIRAFFE: representing scenes as compositional generative neural feature fields. In: CVPR (2021)

    Google Scholar 

  53. 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: CVPR (2022)

    Google Scholar 

  54. Pan, X., Xu, X., Loy, C.C., Theobalt, C., Dai, B.: A shading-guided generative implicit model for shape-accurate 3d-aware image synthesis. In: NeurIPS (2021)

    Google Scholar 

  55. Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: CVPR (2019)

    Google Scholar 

  56. Peng, S., et al.: Animatable neural radiance fields for human body modeling. In: ICCV (2021)

    Google Scholar 

  57. Peng, S.,et al.: Neural body: implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In: CVPR (2021)

    Google Scholar 

  58. Reiser, C., Peng, S., Liao, Y., Geiger, A.: Kilonerf: speeding up neural radiance fields with thousands of tiny mlps. In: ICCV (2021)

    Google Scholar 

  59. Ren, Y., Yu, X., Chen, J., Li, T.H., Li, G.: Deep image spatial transformation for person image generation. In: CVPR (2020)

    Google Scholar 

  60. Rezende, D.J., Eslami, S.M.A., Mohamed, S., Battaglia, P., Jaderberg, M., Heess, N.: Unsupervised learning of 3d structure from images. In: NeurIPS (2016)

    Google Scholar 

  61. Sanyal, S., et al.: Learning realistic human reposing using cyclic self-supervision with 3d shape, pose, and appearance consistency. In: ICCV (2021)

    Google Scholar 

  62. Sanyal, S., et al.: Learning realistic human reposing using cyclic self-supervision with 3d shape, pose, and appearance consistency. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11138–11147 (2021)

    Google Scholar 

  63. Sarkar, K., Golyanik, V., Liu, L., Theobalt, C.: Style and pose control for image synthesis of humans from a single monocular view. arXiv preprint. arXiv:2102.11263 (2021)

  64. Sarkar, K., Liu, L., Golyanik, V., Theobalt, C.: Humangan: a generative model of humans images (2021)

    Google Scholar 

  65. Sarkar, K., Mehta, D., Xu, W., Golyanik, V., Theobalt, C.: Neural re-rendering of humans from a single image. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 596–613. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_35

    Chapter  Google Scholar 

  66. Schwarz, K., Liao, Y., Niemeyer, M., Geiger, A.: Graf: generative radiance fields for 3d-aware image synthesis. In: NeurIPS (2020)

    Google Scholar 

  67. Shen, Y., Gu, J., Tang, X., Zhou, B.: Interpreting the latent space of gans for semantic face editing. In: CVPR (2020)

    Google Scholar 

  68. Shen, Y., Zhou, B.: Closed-form factorization of latent semantics in gans. In: CVPR (2021)

    Google Scholar 

  69. Siarohin, A., Lathuilière, S., Sangineto, E., Sebe, N.: Appearance and pose-conditioned human image generation using deformable GANs. IEEE TPAMI 43(4), 1156–1171 (2020)

    Article  Google Scholar 

  70. Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., Sebe, N.: First order motion model for image animation. In: NeurIPS (2019)

    Google Scholar 

  71. Song, S., Zhang, W., Liu, J., Mei, T.: Unsupervised person image generation with semantic parsing transformation. In: CVPR (2019)

    Google Scholar 

  72. Sun, J., Wang, X., Zhang, Y., Li, X., Zhang, Q., Liu, Y., Wang, J.: Fenerf: face editing in neural radiance fields. arXiv preprint. arXiv:2111.15490 (2021)

  73. Tan, F., et al.: Volux-gan: a generative model for 3d face synthesis with HDRI relighting. arXiv preprint. arXiv:2201.04873 (2022)

  74. Tang, H., Bai, S., Zhang, L., Torr, P.H.S., Sebe, N.: XingGAN for person image generation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 717–734. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58595-2_43

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  76. Vaswani, A., et al.: Attention is all you need. In: NeurIPS (2017)

    Google Scholar 

  77. Wang, X., Gupta, A.: Generative image modeling using style and structure adversarial networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 318–335. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_20

    Chapter  Google Scholar 

  78. Wu, J., Zhang, C., Xue, T., Freeman, W.T., Tenenbaum, J.B.: Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. In: NeurIPS (2016)

    Google Scholar 

  79. Xu, X., Pan, X., Lin, D., Dai, B.: Generative occupancy fields for 3d surface-aware image synthesis. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)

    Google Scholar 

  80. Xu, Y., Peng, S., Yang, C., Shen, Y., Zhou, B.: 3d-aware image synthesis via learning structural and textural representations. In: CVPR (2022)

    Google Scholar 

  81. Yildirim, G., Jetchev, N., Vollgraf, R., Bergmann, U.: Generating high-resolution fashion model images wearing custom outfits. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (2019)

    Google Scholar 

  82. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)

    Google Scholar 

  83. Zhou, P., Xie, L., Ni, B., Tian, Q.: CIPS-3D: a 3D-aware generator of gans based on conditionally-independent pixel synthesis (2021)

    Google Scholar 

  84. Zhou, X., et al.: Cocosnet v2: full-resolution correspondence learning for image translation. In: CVPR (2021)

    Google Scholar 

  85. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)

    Google Scholar 

  86. Zhu, J.Y., et al.: Visual object networks: image generation with disentangled 3D representations. In: NeurIPS (2018)

    Google Scholar 

  87. Zhu, Z., Huang, T., Shi, B., Yu, M., Wang, B., Bai, X.: Progressive pose attention transfer for person image generation. In: CVPR (2019)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the EU H2020 projects AI4Media (No. 951911) and SPRING (No. 871245).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jichao Zhang .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 6729 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

Zhang, J. et al. (2022). 3D-Aware Semantic-Guided Generative Model for Human 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 13675. Springer, Cham. https://doi.org/10.1007/978-3-031-19784-0_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19784-0_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19783-3

  • Online ISBN: 978-3-031-19784-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics