Skip to main content

BlobGAN: Spatially Disentangled Scene Representations

  • 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

We propose an unsupervised, mid-level representation for a generative model of scenes. The representation is mid-level in that it is neither per-pixel nor per-image; rather, scenes are modeled as a collection of spatial, depth-ordered “blobs” of features. Blobs are differentiably placed onto a feature grid that is decoded into an image by a generative adversarial network. Due to the spatial uniformity of blobs and the locality inherent to convolution, our network learns to associate different blobs with different entities in a scene and to arrange these blobs to capture scene layout. We demonstrate this emergent behavior by showing that, despite training without any supervision, our method enables applications such as easy manipulation of objects within a scene (e.g. moving, removing, and restyling furniture), creation of feasible scenes given constraints (e.g. plausible rooms with drawers at a particular location), and parsing of real-world images into constituent parts. On a challenging multi-category dataset of indoor scenes, BlobGAN outperforms StyleGAN2 in image quality as measured by FID. See our project page for video results and interactive demo: http://www.dave.ml/blobgan.

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

Access this chapter

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: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4432–4441 (2019)

    Google Scholar 

  2. 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. (TOG) 40(3), 1–21 (2021)

    Article  Google Scholar 

  3. AlBahar, B., Lu, J., Yang, J., Shu, Z., Shechtman, E., Huang, J.B.: Pose with style: detail-preserving pose-guided image synthesis with conditional StyleGAN. ACM Trans. Graph. 40, 1–11 (2021)

    Article  Google Scholar 

  4. Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28(3), 24 (2009)

    Article  Google Scholar 

  5. Bau, D., Liu, S., Wang, T., Zhu, J.-Y., Torralba, A.: Rewriting a deep generative model. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 351–369. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_21

    Chapter  Google Scholar 

  6. Bau, D., et al.: Gan dissection: visualizing and understanding generative adversarial networks. arXiv preprint arXiv:1811.10597 (2018)

  7. Bau, D., et al.: Seeing what a GAN cannot generate. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4502–4511 (2019)

    Google Scholar 

  8. Bear, D., et al.: Learning physical graph representations from visual scenes. Adv. Neural. Inf. Process. Syst. 33, 6027–6039 (2020)

    Google Scholar 

  9. Biederman, I.: On the semantics of a glance at a scene (1981)

    Google Scholar 

  10. Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis (2018)

    Google Scholar 

  11. Brooks, T., Efros, A.A.: Hallucinating pose-compatible scenes. arXiv preprint arXiv:2112.06909 (2021)

  12. Carson, C., Thomas, M., Belongie, S., Hellerstein, J.M., Malik, J.: Blobworld: a system for region-based image indexing and retrieval. In: Huijsmans, D.P., Smeulders, A.W.M. (eds.) VISUAL 1999. LNCS, vol. 1614, pp. 509–517. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48762-X_63

    Chapter  Google Scholar 

  13. Chai, L., Wulff, J., Isola, P.: Using latent space regression to analyze and leverage compositionality in GANs. arXiv preprint arXiv:2103.10426 (2021)

  14. Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1511–1520 (2017)

    Google Scholar 

  15. Collins, E., Bala, R., Price, B., Susstrunk, S.: Editing in style: uncovering the local semantics of GANs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5771–5780 (2020)

    Google Scholar 

  16. Denton, E.L., Chintala, S., Fergus, R., et al.: Deep generative image models using laplacian pyramid of adversarial networks. Adv. Neural Inf. Process. Syst. 28 (2015)

    Google Scholar 

  17. Goetschalckx, L., Andonian, A., Oliva, A., Isola, P.: GANalyze: toward visual definitions of cognitive image properties. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5744–5753 (2019)

    Google Scholar 

  18. Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014)

    Google Scholar 

  19. Gupta, A., Efros, A.A., Hebert, M.: Blocks world revisited: image understanding using qualitative geometry and mechanics. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 482–496. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_35

    Chapter  Google Scholar 

  20. Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 297–312. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10584-0_20

    Chapter  Google Scholar 

  21. Härkönen, E., Hertzmann, A., Lehtinen, J., Paris, S.: GANSpace: discovering interpretable GAN controls. Adv. Neural. Inf. Process. Syst. 33, 9841–9850 (2020)

    Google Scholar 

  22. He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377 (2021)

  23. He, X., Wandt, B., Rhodin, H.: Latentkeypointgan: controlling GANs via latent keypoints. arXiv preprint arXiv:2103.15812 (2021)

  24. Hedau, V., Hoiem, D., Forsyth, D.: Recovering the spatial layout of cluttered rooms. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1849–1856. IEEE (2009)

    Google Scholar 

  25. 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. Adv. Neural Inf. Process. Syst. 30 (2017)

    Google Scholar 

  26. Higgins, I., et al.: Beta-VAE: learning basic visual concepts with a constrained variational framework (2016)

    Google Scholar 

  27. Hock, H.S., Romanski, L., Galie, A., Williams, C.S.: Real-world schemata and scene recognition in adults and children. Mem. Cogn. 6(4), 423–431 (1978)

    Article  Google Scholar 

  28. Hoiem, D., Efros, A.A., Hebert, M.: Recovering surface layout from an image. IJCV 75(1), 151–172 (2007)

    Article  MATH  Google Scholar 

  29. Huang, X., Liu, M.-Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 179–196. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_11

    Chapter  Google Scholar 

  30. Isola, P., Liu, C.: Scene collaging: analysis and synthesis of natural images with semantic layers. In: ICCV (2013)

    Google Scholar 

  31. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)

    Google Scholar 

  32. Jahanian, A., Chai, L., Isola, P.: On the “steerability” of generative adversarial networks. arXiv preprint arXiv:1907.07171 (2019)

  33. Johnson, J., Gupta, A., Fei-Fei, L.: Image generation from scene graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1219–1228 (2018)

    Google Scholar 

  34. Johnson, J., Hariharan, B., Van Der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., Girshick, R.: CLEVR: a diagnostic dataset for compositional language and elementary visual reasoning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2901–2910 (2017)

    Google Scholar 

  35. Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation (2018)

    Google Scholar 

  36. Karras, T., et al.: Alias-free generative adversarial networks. Adv. Neural Inf. Process. Syst. 34 (2021)

    Google Scholar 

  37. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)

    Google Scholar 

  38. Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110–8119 (2020)

    Google Scholar 

  39. Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN (2020)

    Google Scholar 

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

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

  42. Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., Aila, T.: Improved precision and recall metric for assessing generative models. Adv. Neural Inf. Process. Syst. 32 (2019)

    Google Scholar 

  43. Lewis, K.M., Varadharajan, S., Kemelmacher-Shlizerman, I.: TryonGAN: body-aware try-on via layered interpolation. ACM Trans. Graph. (Proc. ACM SIGGRAPH 2021) 40(4), 1–10 (2021)

    Google Scholar 

  44. Li, K., Zhang, T., Malik, J.: Diverse image synthesis from semantic layouts via conditional IMLE. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4220–4229 (2019)

    Google Scholar 

  45. Li, Y., Li, Y., Lu, J., Shechtman, E., Lee, Y.J., Singh, K.K.: Collaging class-specific GANs for semantic image synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14418–14427 (2021)

    Google Scholar 

  46. Locatello, F., et al.: Challenging common assumptions in the unsupervised learning of disentangled representations. In: International Conference on Machine Learning, pp. 4114–4124. PMLR (2019)

    Google Scholar 

  47. Malisiewicz, T., Efros, A.: Beyond categories: the visual memex model for reasoning about object relationships. Adv. Neural Inf. Process. Syst. 22 (2009)

    Google Scholar 

  48. Mejjati, Y.A., Milefchik, I., Gokaslan, A., Wang, O., Kim, K.I., Tompkin, J.: GaussiGAN: controllable image synthesis with 3D gaussians from unposed silhouettes. arXiv preprint arXiv:2106.13215 (2021)

  49. Mejjati, Y.A., et al.: Generating object stamps. In: Computer Vision and Pattern Recognition Workshop on AI for Content Creation (CVPRW) (2020)

    Google Scholar 

  50. Meng, C., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: SDEdit: image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021)

  51. Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., Yang, Y.L.: HoloGAN: unsupervised learning of 3D representations from natural images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7588–7597 (2019)

    Google Scholar 

  52. Nguyen-Phuoc, T.H., Richardt, C., Mai, L., Yang, Y., Mitra, N.: BlockGAN: learning 3D object-aware scene representations from unlabelled images. Adv. Neural. Inf. Process. Syst. 33, 6767–6778 (2020)

    Google Scholar 

  53. Nichol, A., et al.: GLIDE: towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741 (2021)

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

    Google Scholar 

  55. Nitzberg, M., Mumford, D.B.: The 2.1-D Sketch. IEEE Computer Society Press (1990)

    Google Scholar 

  56. Ohta, Y., Kanade, T., Sakai, T.: An analysis system for scenes containing objects with substructures. In: Proceedings of 4th International Joint Conference on Pattern Recognition (IJCPR 1978), pp. 752–754 (1978)

    Google Scholar 

  57. Oktay, D., Vondrick, C., Torralba, A.: Counterfactual image networks (2018). https://openreview.net/forum?id=SyYYPdg0-

  58. Oliva, A., Torralba, A.: The role of context in object recognition. Trends Cogn. Sci. 11(12), 520–527 (2007)

    Article  Google Scholar 

  59. Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2337–2346 (2019)

    Google Scholar 

  60. Park, T., et al.: Swapping autoencoder for deep image manipulation. Adv. Neural. Inf. Process. Syst. 33, 7198–7211 (2020)

    Google Scholar 

  61. Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., Lischinski, D.: StyleCLIP: text-driven manipulation of StyleGAN imagery. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2085–2094 (2021)

    Google Scholar 

  62. Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.: Context encoders: feature learning by inpainting (2016)

    Google Scholar 

  63. Peebles, W., Peebles, J., Zhu, J.-Y., Efros, A., Torralba, A.: The hessian penalty: a weak prior for unsupervised disentanglement. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 581–597. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58539-6_35

    Chapter  Google Scholar 

  64. Porter, T., Duff, T.: Compositing digital images. In: Proceedings of the 11th annual Conference on Computer Graphics and Interactive Techniques, pp. 253–259 (1984)

    Google Scholar 

  65. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)

  66. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 (2022)

  67. Ramesh, A., et al.: Zero-shot text-to-image generation. In: International Conference on Machine Learning, pp. 8821–8831. PMLR (2021)

    Google Scholar 

  68. Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. In: International Conference on Machine Learning, pp. 1060–1069. PMLR (2016)

    Google Scholar 

  69. Richardson, E., et al.: Encoding in style: a StyleGAN encoder for image-to-image translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2287–2296 (2021)

    Google Scholar 

  70. Roich, D., Mokady, R., Bermano, A.H., Cohen-Or, D.: Pivotal tuning for latent-based editing of real images. arXiv preprint arXiv:2106.05744 (2021)

  71. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. arXiv preprint arXiv:2112.10752 (2021)

  72. Rott Shaham, T., Dekel, T., Michaeli, T.: SinGAN: learning a generative model from a single natural image. In: IEEE International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  73. Russell, B., Efros, A., Sivic, J., Freeman, B., Zisserman, A.: Segmenting scenes by matching image composites. Adv. Neural Inf. Process. Syst. 22 (2009)

    Google Scholar 

  74. Saharia, C., et al.: Palette: Image-to-image diffusion models. arXiv preprint arXiv:2111.05826 (2021)

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

    Google Scholar 

  76. Shen, Y., Yang, C., Tang, X., Zhou, B.: InterFaceGAN: interpreting the disentangled face representation learned by GANs. IEEE Trans. Pattern Anal. Mach. Intell. (2020)

    Google Scholar 

  77. Shen, Y., Zhou, B.: Closed-form factorization of latent semantics in GANs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1532–1540 (2021)

    Google Scholar 

  78. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)

    Article  Google Scholar 

  79. Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., Sebe, N.: First order motion model for image animation. In: Conference on Neural Information Processing Systems (NeurIPS) (2019)

    Google Scholar 

  80. Siarohin, A., Woodford, O., Ren, J., Chai, M., Tulyakov, S.: Motion representations for articulated animation. In: CVPR (2021)

    Google Scholar 

  81. Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_54

    Chapter  Google Scholar 

  82. Simakov, D., Caspi, Y., Shechtman, E., Irani, M.: Summarizing visual data using bidirectional similarity. In: CVPR. IEEE Computer Society (2008)

    Google Scholar 

  83. Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: transformer for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7262–7272 (2021)

    Google Scholar 

  84. Sudderth, E.B., Torralba, A., Freeman, W.T., Willsky, A.S.: Learning hierarchical models of scenes, objects, and parts. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005) Volume 1, vol. 2, pp. 1331–1338. IEEE (2005)

    Google Scholar 

  85. Torralba, A., Willsky, A., Sudderth, E., Freeman, W.: Describing visual scenes using transformed Dirichlet processes. Adv. Neural Inf. Process. Syst. 18 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  87. Tu, Z., Chen, X., Yuille, A.L., Zhu, S.C.: Image parsing: unifying segmentation, detection, and recognition. Int. J. Comput. Vis. 63(2), 113–140 (2005)

    Article  Google Scholar 

  88. Wang, J., Yang, C., Xu, Y., Shen, Y., Li, H., Zhou, B.: Improving GAN equilibrium by raising spatial awareness. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11285–11293 (2022)

    Google Scholar 

  89. Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8798–8807 (2018)

    Google Scholar 

  90. Wu, Z., Lischinski, D., Shechtman, E.: StyleSpace analysis: disentangled controls for StyleGAN image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12863–12872 (2021)

    Google Scholar 

  91. Yakimovsky, Y., Feldman, J.A.: A semantics-based decision theory region analyser. In: IJCAI, pp. 580–588. William Kaufmann (1973)

    Google Scholar 

  92. Yang, C., Shen, Y., Zhou, B.: Semantic hierarchy emerges in deep generative representations for scene synthesis. Int. J. Comput. Vis. 129(5), 1451–1466 (2021)

    Article  Google Scholar 

  93. Yao, S., et al.: 3D-aware scene manipulation via inverse graphics. Adv. Neural Inf. Process. Syst. 31 (2018)

    Google Scholar 

  94. Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., Xiao, J.: LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365 (2015)

  95. Yu, H.X., Guibas, L.J., Wu, J.: Unsupervised discovery of object radiance fields. arXiv preprint arXiv:2107.07905 (2021)

  96. Yu, S.X., Gross, R., Shi, J.: Concurrent object recognition and segmentation by graph partitioning. Adv. Neural Inf. Process. Syst. 15 (2002)

    Google Scholar 

  97. Zhang, C., Xu, Y., Shen, Y.: Decorating your own bedroom: locally controlling image generation with generative adversarial networks. arXiv preprint arXiv:2105.08222 (2021)

  98. Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. In: International conference on machine learning, pp. 7354–7363. PMLR (2019)

    Google Scholar 

  99. Zhang, H., et al.: StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5907–5915 (2017)

    Google Scholar 

  100. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018)

    Google Scholar 

  101. Zhu, J., Shen, Y., Xu, Y., Zhao, D., Chen, Q.: Region-based semantic factorization in GANs. arXiv preprint arXiv:2202.09649 (2022)

  102. Zhu, J.-Y., Krähenbühl, P., Shechtman, E., Efros, A.A.: Generative visual manipulation on the natural image manifold. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 597–613. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_36

    Chapter  Google Scholar 

  103. Zhu, J.Y., et al.: Toward multimodal image-to-image translation. Adv. Neural Inf. Process. Syst. 30 (2017)

    Google Scholar 

Download references

Acknowledgements

We thank Allan Jabri, Assaf Shocher, Bill Peebles, Tim Brooks, and Yossi Gandelsman for endless insightful discussions and important feedback, and especially thank Vickie Ye for advice on blob compositing, splatting, and visualization. Thanks also to Georgios Pavlakos for deadline-week pixel inpsection and Shiry Ginosar for post-deadline-week guidance and helpful comments. Research was supported in part by the DARPA MCS program and a gift from Adobe Research. This work was started while DE was an intern at Adobe.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dave Epstein .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 15216 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

Epstein, D., Park, T., Zhang, R., Shechtman, E., Efros, A.A. (2022). BlobGAN: Spatially Disentangled Scene Representations. 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_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19784-0_36

  • 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