The Visual Computer

, Volume 35, Issue 6–8, pp 783–795 | Cite as

Expressive facial style transfer for personalized memes mimic

  • Yanlong Tang
  • Xiaoguang HanEmail author
  • Yue Li
  • Liqian Ma
  • Ruofeng Tong
Original Article


Meme, usually represented by an image of exaggerated expressive face captioned with short text, are increasingly produced and used online to express people’s strong or subtle emotions. Meanwhile, meme mimic apps continuously appear, such as the meme filming feature in WeChat App that allow users to imitate meme expressions. Motivated by such scenarios, we focus on transferring exaggerated or unique expressions which is rarely noticed by previous works. We present a technique—“expressive style transfer”—which allows users to faithfully imitate popular memes’ unique expression styles both geometrically and textually. To conduct distortion-free transferring of exaggerated geometry, we propose a novel accurate feature curve-based face reconstruction algorithm for 3D-aware image warping. Furthermore, we propose an identity preserving blending model, based on a deep neural network, to enhance facial expressive textural details. We demonstrate the effectiveness of our method on a collection of Internet memes.


Expressive style transfer Meme generation Curve-based 3D face reconstruction Neural-style alpha-blending 



We thank the anonymous reviewers for the insightful and constructive comments, Matt Boyd-Surka for proofreading this manuscript, Jinghui Zhou, Keli Cheng and Xiaodong Gu for valuable discussions, and Yuan Yao for providing their deep image analogy result [29]. This paper was supported by the National Natural Science Foundation of China (No. 61832016) and the Science and Technology Project of Zhejiang province (No.2018C01080). This work was also funded in part by the Pearl River Talent Recruitment Program Innovative and Entrepreneurial Teams in 2017 under Grant No. 2017ZT07X152 and the Shenzhen Fundamental Research Fund under Grants No. KQTD2015033114415450 and No. ZDSYS201707251409055.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Averbuch-Elor, H., Cohen-Or, D., Kopf, J., Cohen, M.F.: Bringing portraits to life. ACM Trans. Graph. (TOG) 36(6), 196 (2017)CrossRefGoogle Scholar
  2. 2.
    Bas, A., Smith, W.A., Bolkart, T., Wuhrer, S.: Fitting a 3d morphable model to edges: a comparison between hard and soft correspondences. In: Asian Conference on Computer Vision, pp. 377–391. Springer (2016)Google Scholar
  3. 3.
    Besl, P.J., McKay, N.D.: Method for registration of 3-d shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611, pp. 586–607. International Society for Optics and Photonics (1992)Google Scholar
  4. 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. ACM Press/Addison-Wesley Publishing Co. (1999)Google Scholar
  5. 5.
    Booth, J., Antonakos, E., Ploumpis, S., Trigeorgis, G., Panagakis, Y., Zafeiriou, S., et al.: 3d face morphable models in-the-wild. In: Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition (2017)Google Scholar
  6. 6.
    Bouaziz, S., Wang, Y., Pauly, M.: Online modeling for realtime facial animation. ACM Trans. Graph. (TOG) 32(4), 40 (2013)CrossRefzbMATHGoogle Scholar
  7. 7.
    Cao, C., Hou, Q., Zhou, K.: Displaced dynamic expression regression for real-time facial tracking and animation. ACM Trans. Graph. (TOG) 33(4), 43 (2014)Google Scholar
  8. 8.
    Cao, C., Weng, Y., Lin, S., Zhou, K.: 3d shape regression for real-time facial animation. ACM Trans. Graph. (TOG) 32(4), 41 (2013)CrossRefzbMATHGoogle Scholar
  9. 9.
    Cao, C., Weng, Y., Zhou, S., Tong, Y., Zhou, K.: Facewarehouse: a 3d facial expression database for visual computing. IEEE Trans. Vis. Comput. Graph. 20(3), 413–425 (2014)CrossRefGoogle Scholar
  10. 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. arXiv preprint 1711 (2017)Google Scholar
  11. 11.
    Ding, L., Ding, X., Fang, C.: 3d face sparse reconstruction based on local linear fitting. Vis. Comput. 30(2), 189–200 (2014)CrossRefGoogle Scholar
  12. 12.
    Fišer, J., Jamriška, O., Simons, D., Shechtman, E., Lu, J., Asente, P., Lukáč, M., Sỳkora, D.: Example-based synthesis of stylized facial animations. ACM Trans. Graph. (TOG) 36(4), 155 (2017)Google Scholar
  13. 13.
    Frigo, O., Sabater, N., Delon, J., Hellier, P.: Video style transfer by consistent adaptive patch sampling. Vis. Comput. 35(3), 429–443 (2019)CrossRefGoogle Scholar
  14. 14.
    Garrido, P., Valgaerts, L., Rehmsen, O., Thormahlen, T., Perez, P., Theobalt, C.: Automatic face reenactment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4217–4224 (2014)Google Scholar
  15. 15.
    Garrido, P., Zollhöfer, M., Casas, D., Valgaerts, L., Varanasi, K., Pérez, P., Theobalt, C.: Reconstruction of personalized 3d face rigs from monocular video. ACM Trans. Graph. (TOG) 35(3), 28 (2016)CrossRefGoogle Scholar
  16. 16.
    Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)Google Scholar
  17. 17.
    Geng, J., Shao, T., Zheng, Y., Weng, Y., Zhou, K.: Warp-guided GANs for single-photo facial animation. In: SIGGRAPH Asia 2018 Technical Papers, p. 231. ACM (2018)Google Scholar
  18. 18.
    Jackson, A.S., Bulat, A., Argyriou, V., Tzimiropoulos, G.: Large pose 3d face reconstruction from a single image via direct volumetric CNN regression. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1031–1039 (2017)Google Scholar
  19. 19.
    Jiang, L., Zhang, J., Deng, B., Li, H., Liu, L.: 3d face reconstruction with geometry details from a single image. arXiv preprint arXiv:1702.05619 (2017)
  20. 20.
    Jing, Y., Yang, Y., Feng, Z., Ye, J., Song, M.: Neural style transfer: a review. CoRR abs/1705.04058 (2017)Google Scholar
  21. 21.
    Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision, pp. 694–711. Springer (2016)Google Scholar
  22. 22.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vision 1(4), 321–331 (1988)CrossRefzbMATHGoogle Scholar
  23. 23.
    Kemelmacher-Shlizerman, I.: Internet based morphable model. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3256–3263 (2013)Google Scholar
  24. 24.
    Kim, H., Garrido, P., Tewari, A., Xu, W., Thies, J., Nießner, M., Pérez, P., Richardt, C., Zollhöfer, M., Theobalt, C.: Deep video portraits. arXiv preprint arXiv:1805.11714 (2018)
  25. 25.
    Kingma, D.P., Dhariwal, P.: Glow: generative flow with invertible \(1\times 1\) convolutions. arXiv preprint arXiv:1807.03039 (2018)
  26. 26.
    Li, C., Wand, M.: Precomputed real-time texture synthesis with Markovian generative adversarial networks. In: European Conference on Computer Vision, pp. 702–716. Springer (2016)Google Scholar
  27. 27.
    Li, H., Yu, J., Ye, Y., Bregler, C.: Realtime facial animation with on-the-fly correctives. ACM Trans. Graph. 32(4), 42–1 (2013)zbMATHGoogle Scholar
  28. 28.
    Li, Y., Ma, L., Fan, H., Mitchell, K.: Feature-preserving detailed 3d face reconstruction from a single image. In: Proceedings of the 15th ACM SIGGRAPH European Conference on Visual Media Production, pp. 1:1–1:9. ACM, New York, NY, USA (2018)Google Scholar
  29. 29.
    Liao, J., Yao, Y., Yuan, L., Hua, G., Kang, S.B.: Visual attribute transfer through deep image analogy. arXiv preprint arXiv:1705.01088 (2017)
  30. 30.
    Luan, F., Paris, S., Shechtman, E., Bala, K.: Deep photo style transfer. CoRR, arXiv:1703.07511 2 (2017)
  31. 31.
    Ma, M., Peng, S., Hu, X.: A lighting robust fitting approach of 3d morphable model for face reconstruction. Vis. Comput. 32(10), 1223–1238 (2016)CrossRefGoogle Scholar
  32. 32.
    Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: BMVC, vol. 1, p. 6 (2015)Google Scholar
  33. 33.
    Pumarola, A., Agudo, A., Martinez, A.M., Sanfeliu, A., Moreno-Noguer, F.: Ganimation: anatomically-aware facial animation from a single image. arXiv preprint arXiv:1807.09251 (2018)
  34. 34.
    Qiao, F., Yao, N., Jiao, Z., Li, Z., Chen, H., Wang, H.: Emotional facial expression transfer from a single image via generative adversarial nets. Comput. Anim. Virtual Worlds 29(3–4), e1819 (2018)CrossRefGoogle Scholar
  35. 35.
    Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graph. Appl. 21(5), 34–41 (2001)CrossRefGoogle Scholar
  36. 36.
    Romdhani, S., Vetter, T.: Estimating 3d shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 2, pp. 986–993. IEEE (2005)Google Scholar
  37. 37.
    Snape, P., Panagakis, Y., Zafeiriou, S.: Automatic construction of robust spherical harmonic subspaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 91–100 (2015)Google Scholar
  38. 38.
    Snape, P., Zafeiriou, S.: Kernel-PCA analysis of surface normals for shape-from-shading. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1059–1066 (2014)Google Scholar
  39. 39.
    Sorkine, O., Cohen-Or, D., Lipman, Y., Alexa, M., Rössl, C., Seidel, H.P.: Laplacian surface editing. In: Proceedings of the 2004 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing, pp. 175–184. ACM (2004)Google Scholar
  40. 40.
    Sumner, R.W., Popović, J.: Deformation transfer for triangle meshes. In: ACM Transactions on Graphics (TOG), vol. 23, pp. 399–405. ACM (2004)Google Scholar
  41. 41.
    Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., Nießner, M.: Face2face: Real-time face capture and reenactment of RGB videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2387–2395 (2016)Google Scholar
  42. 42.
    Wang, L., Wang, Z., Yang, X., Hu, S.M., Zhang, J.: Photographic style transfer. Vis. Comput. (2018).
  43. 43.
    Wu, W., Zhang, Y., Li, C., Qian, C., Loy, C.C.: Reenactgan: Learning to reenact faces via boundary transfer. arXiv preprint arXiv:1807.11079 (2018)
  44. 44.
    Yang, F., Wang, J., Shechtman, E., Bourdev, L., Metaxas, D.: Expression flow for 3d-aware face component transfer. ACM Trans. Graph. (TOG) 30(4), 60 (2011)CrossRefGoogle Scholar
  45. 45.
    Zhu, X., Lei, Z., Liu, X., Shi, H., Li, S.Z.: Face alignment across large poses: A 3d solution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 146–155 (2016)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Yanlong Tang
    • 1
  • Xiaoguang Han
    • 2
    Email author
  • Yue Li
    • 3
  • Liqian Ma
    • 4
  • Ruofeng Tong
    • 1
  1. 1.Zhejiang UniversityHangzhouChina
  2. 2.Shenzhen Research Institute of Big DataThe Chinese University of Hong Kong (Shenzhen)ShenzhenChina
  3. 3.University of PennsylvaniaPhiladelphiaUSA
  4. 4.Beijing Kuaishou Technology Ltd.BeijingChina

Personalised recommendations