Unselfie: Translating Selfies to Neutral-Pose Portraits in the Wild

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12362)


Due to the ubiquity of smartphones, it is popular to take photos of one’s self, or “selfies.” Such photos are convenient to take, because they do not require specialized equipment or a third-party photographer. However, in selfies, constraints such as human arm length often make the body pose look unnatural. To address this issue, we introduce unselfie, a novel photographic transformation that automatically translates a selfie into a neutral-pose portrait. To achieve this, we first collect an unpaired dataset, and introduce a way to synthesize paired training data for self-supervised learning. Then, to unselfie a photo, we propose a new three-stage pipeline, where we first find a target neutral pose, inpaint the body texture, and finally refine and composite the person on the background. To obtain a suitable target neutral pose, we propose a novel nearest pose search module that makes the reposing task easier and enables the generation of multiple neutral-pose results among which users can choose the best one they like. Qualitative and quantitative evaluations show the superiority of our pipeline over alternatives.


Image editing Selfie Human pose transfer 



This work was partially funded by Adobe Research. We thank He Zhang for helping mask estimation. Selfie photo owners: #139639837-Baikal360, #224341474-Drobot Dean, #153081973-MaximBeykov, #67229337-Oleg Shelomentsev, #194139222-Syda Productions, #212727509-Photocatcher, #168103021-sosiukin, #162277318-rh2010, #225137362-BublikHaus, #120915150-wollertz, #133457041-ilovemayorova, #109067715-Tupungato, #121680430-Mego-studio, #206713499-Paolese –

Supplementary material

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Supplementary material 1 (pdf 7783 KB)


  1. 1.
    Alp Güler, R., Neverova, N., Kokkinos, I.: Densepose: dense human pose estimation in the wild. In: CVPR (2018)Google Scholar
  2. 2.
    Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein gan. In: ICLR (2017)Google Scholar
  3. 3.
    Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. (TOG) 28(3), 24 (2009)CrossRefGoogle Scholar
  4. 4.
    Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying MMD GANs. In: ICLR (2018)Google Scholar
  5. 5.
    Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: ICLR (2019)Google Scholar
  6. 6.
    Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. In: arXiv preprint arXiv:1812.08008 (2018)
  7. 7.
    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
  8. 8.
    Darabi, S., Shechtman, E., Barnes, C., Goldman, D.B., Sen, P.: Image melding: combining inconsistent images using patch-based synthesis. ACM Trans. Graph. (TOG) 31(4), 82:1–82:10 (2012)CrossRefGoogle Scholar
  9. 9.
    Dong, H., Liang, X., Gong, K., Lai, H., Zhu, J., Yin, J.: Soft-gated warping-gan for pose-guided person image synthesis. In: NeurIPS (2018)Google Scholar
  10. 10.
    Esser, P., Sutter, E., Ommer, B.: A variational U-net for conditional appearance and shape generation. In: CVPR (2018)Google Scholar
  11. 11.
    Ge, Y., Zhang, R., Wu, L., Wang, X., Tang, X., Luo, P.: A versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images (2019)Google Scholar
  12. 12.
    Good, P.: Permutation tests: a practical guide to resampling methods for testing hypotheses. Springer Science & Business Media (2000)Google Scholar
  13. 13.
    Goodfellow, I., et al.: Generative adversarial nets. In: NIPS (2014)Google Scholar
  14. 14.
    Grigorev, A., Sevastopolsky, A., Vakhitov, A., Lempitsky, V.: Coordinate-based texture inpainting for pose-guided image generation. In: CVPR (2019)Google Scholar
  15. 15.
    Han, X., Hu, X., Huang, W., Scott, M.R.: Clothflow: A flow-based model for clothed person generation. In: ICCV (2019)Google Scholar
  16. 16.
    He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)Google Scholar
  17. 17.
    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: NIPS (2017)Google Scholar
  18. 18.
    Huang, J.B., Kang, S.B., Ahuja, N., Kopf, J.: Image completion using planar structure guidance. ACM Trans. Graph. (TOG) 33(4), 129 (2014)Google Scholar
  19. 19.
    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). Scholar
  20. 20.
    Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)Google Scholar
  21. 21.
    Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR (2019)Google Scholar
  22. 22.
    Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: ICLR (2014)Google Scholar
  23. 23.
    Lassner, C., Pons-Moll, G., Gehler, P.V.: A generative model of people in clothing. In: ICCV (2017)Google Scholar
  24. 24.
    Lee, H.-Y., Tseng, H.-Y., Huang, J.-B., Singh, M., Yang, M.-H.: Diverse image-to-image translation via disentangled representations. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 36–52. Springer, Cham (2018). Scholar
  25. 25.
    Liang, X., et al.: Deep human parsing with active template regression. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 37(12), 2402–2414 (2015)CrossRefGoogle Scholar
  26. 26.
    Liang, X., Zhang, H., Lin, L., Xing, E.: Generative semantic manipulation with mask-contrasting GAN. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 574–590. Springer, Cham (2018). Scholar
  27. 27.
    Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). Scholar
  28. 28.
    Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: NIPS (2017)Google Scholar
  29. 29.
    Liu, W., Piao, Z., Min, J., Luo, W., Ma, L., Gao, S.: Liquid warping gan: a unified framework for human motion imitation, appearance transfer and novel view synthesis. In: ICCV (2019)Google Scholar
  30. 30.
    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
  31. 31.
    Ma, L., Jia, X., Georgoulis, S., Tuytelaars, T., Van Gool, L.: Exemplar guided unsupervised image-to-image translation with semantic consistency. In: ICLR (2019)Google Scholar
  32. 32.
    Ma, L., Sun, Q., Georgoulis, S., Van Gool, L., Schiele, B., Fritz, M.: Disentangled person image generation. In: CVPR (2018)Google Scholar
  33. 33.
    Ma, L., Xu, J., Sun, Q., Schiele, B., Tuytelaars, T., Van Gool, L.: Pose guided person image generation. In: NIPS (2017)Google Scholar
  34. 34.
    Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: ICCV (2017)Google Scholar
  35. 35.
    Mejjati, Y.A., Richardt, C., Tompkin, J., Cosker, D., Kim, K.I.: Unsupervised attention-guided image-to-image translation. In: NeurIPS (2018)Google Scholar
  36. 36.
    Men, Y., Mao, Y., Jiang, Y., Ma, W.Y., Lian, Z.: Controllable person image synthesis with attribute-decomposed GAN. In: CVPR (2020)Google Scholar
  37. 37.
    Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
  38. 38.
    Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. (CVIU) 104(2), 90–126 (2006)CrossRefGoogle Scholar
  39. 39.
    Nazeri, K., Ng, E., Joseph, T., Qureshi, F., Ebrahimi, M.: EdgeConnect: structure guided image inpainting using edge prediction. In: ICCV Workshops (2019)Google Scholar
  40. 40.
    Neverova, N., Alp Güler, R., Kokkinos, I.: Dense pose transfer. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 128–143. Springer, Cham (2018). Scholar
  41. 41.
    Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: CVPR (2016)Google Scholar
  42. 42.
    Pumarola, A., Agudo, A., Sanfeliu, A., Moreno-Noguer, F.: Unsupervised person image synthesis in arbitrary poses. In: CVPR (2018)Google Scholar
  43. 43.
    Qi, X., Chen, Q., Jia, J., Koltun, V.: Semi-parametric image synthesis. In: CVPR (2018)Google Scholar
  44. 44.
    Qian, S., et al.: Make a face: towards arbitrary high fidelity face manipulation. In: ICCV (2019)Google Scholar
  45. 45.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: ICLR (2016)Google Scholar
  46. 46.
    Raj, A., Sangkloy, P., Chang, H., Hays, J., Ceylan, D., Lu, J.: SwapNet: image based garment transfer. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 679–695. Springer, Cham (2018). Scholar
  47. 47.
    Reed, S.E., Akata, Z., Mohan, S., Tenka, S., Schiele, B., Lee, H.: Learning what and where to draw. In: NIPS (2016)Google Scholar
  48. 48.
    Reed, S.E., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. In: ICML (2016)Google Scholar
  49. 49.
    Ren, Y., Yu, X., Chen, J., Li, T.H., Li, G.: Deep image spatial transformation for person image generation. In: CVPR (2020)Google Scholar
  50. 50.
    Siarohin, A., Sangineto, E., Lathuilière, S., Sebe, N.: Deformable gans for pose-based human image generation. In: CVPR (2018)Google Scholar
  51. 51.
    Song, S., Zhang, W., Liu, J., Mei, T.: Unsupervised person image generation with semantic parsing transformation. In: CVPR (2019)Google Scholar
  52. 52.
    Weng, S., Li, W., Li, D., Jin, H., Shi, B.: Misc: Multi-condition injection and spatially-adaptive compositing for conditional person image synthesis. In: CVPR (2020)Google Scholar
  53. 53.
    Wu, W., Cao, K., Li, C., Qian, C., Loy, C.C.: Transgaga: Geometry-aware unsupervised image-to-image translation. In: CVPR (2019)Google Scholar
  54. 54.
    Xu, N., Price, B., Cohen, S., Huang, T.: Deep image matting. In: CVPR (2017)Google Scholar
  55. 55.
    Xu, Z., Sun, J.: Image inpainting by patch propagation using patch sparsity. IEEE Trans. Image Process. (TIP) 19(5), 1153–1165 (2010)MathSciNetCrossRefGoogle Scholar
  56. 56.
    Yan, Z., Li, X., Li, M., Zuo, W., Shan, S.: Shift-net: image inpainting via deep feature rearrangement. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 3–19. Springer, Cham (2018). Scholar
  57. 57.
    Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.S.: Generative image inpainting with contextual attention. In: CVPR (2018)Google Scholar
  58. 58.
    Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.S.: Free-form image inpainting with gated convolution. In: ICCV (2019)Google Scholar
  59. 59.
    Zeng, Y., Lin, Z., Yang, J., Zhang, J., Shechtman, E., Lu, H.: High-resolution image inpainting with iterative confidence feedback and guided upsampling. In: ECCV (2020)Google Scholar
  60. 60.
    Zhang, H., et al.: Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: ICCV (2017)Google Scholar
  61. 61.
    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
  62. 62.
    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
  63. 63.
    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

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.KU LeuvenLeuvenBelgium
  2. 2.Adobe ResearchAntwerpBelgium
  3. 3.UC BerkeleyBerkeleyUSA

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