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GANimation: One-Shot Anatomically Consistent Facial Animation


Recent advances in generative adversarial networks (GANs) have shown impressive results for the task of facial expression synthesis. The most successful architecture is StarGAN (Choi et al. in CVPR, 2018), that conditions GANs’ generation process with images of a specific domain, namely a set of images of people sharing the same expression. While effective, this approach can only generate a discrete number of expressions, determined by the content and granularity of the dataset. To address this limitation, in this paper, we introduce a novel GAN conditioning scheme based on action units (AU) annotations, which describes in a continuous manifold the anatomical facial movements defining a human expression. Our approach allows controlling the magnitude of activation of each AU and combining several of them. Additionally, we propose a weakly supervised strategy to train the model, that only requires images annotated with their activated AUs, and exploit a novel self-learned attention mechanism that makes our network robust to changing backgrounds, lighting conditions and occlusions. Extensive evaluation shows that our approach goes beyond competing conditional generators both in the capability to synthesize a much wider range of expressions ruled by anatomically feasible muscle movements, as in the capacity of dealing with images in the wild. The code of this work is publicly available at

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    The dataset was re-annotated with Baltrušaitis et al. (2015) to obtain continuous activation annotations.

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    We use the face detector from


  1. Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN. arXiv preprint arXiv:1701.07875

  2. Baltrušaitis, T., Mahmoud, M., & Robinson, P. (2015). Cross-dataset learning and person-specific normalisation for automatic action unit detection. In FG.

  3. Benitez-Quiroz, C. F., Srinivasan, R., & Martinez, A. M. (2016). Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. In CVPR.

  4. Blanz, V., & Vetter, T. (1999). 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). New York: ACM Press.

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

  6. Dolhansky, B., & Canton Ferrer, C. (2018). Eye in-painting with exemplar generative adversarial networks. In CVPR.

  7. Du, S., Tao, Y., & Martinez, A. M. (2014). Compound facial expressions of emotion. Proceedings of the National Academy of Sciences, 111(15), E1454–E1462.

    Article  Google Scholar 

  8. Ekman, P., & Friesen, W. (1978). Facial action coding system: A technique for the measurement of facial movement. Palo Altom: Consulting Psychologists Press.

    Google Scholar 

  9. Fischler, M. A., & Elschlager, R. A. (1973). The representation and matching of pictorial structures. IEEE Transactions on Computers, 22(1), 67–92.

    Article  Google Scholar 

  10. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al. (2014). Generative adversarial nets. In NIPS.

  11. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. C. (2017). Improved training of wasserstein GANs. In NIPS.

  12. Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A.: Image-to-image translation with conditional adversarial networks. In CVPR (2017)

  13. Johnson, J., Alahi, A., & Fei-Fei, L. (2016). Perceptual losses for real-time style transfer and super-resolution. In ECCV.

  14. Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2018). Progressive growing of GANs for improved quality, stability, and variation. In ICLR.

  15. Kim, T., Cha, M., Kim, H., Lee, J., & Kim, J. (2017). Learning to discover cross-domain relations with generative adversarial networks. In ICML.

  16. Kim, H., Garrido, P., Tewari, A., Xu, W., Thies, J., Nießner, N., et al. (2018). Deep video portraits. ACM Transactions on Graphics, 37, 163.

    Google Scholar 

  17. Kingma, D., & Ba, J. (2015). ADAM: A method for stochastic optimization. In ICLR.

  18. Kingma, D. P., & Welling, M. (2014). Auto-encoding variational bayes. In ICLR.

  19. Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D. H., Hawk, S. T., & Van Knippenberg, A. (2010). Presentation and validation of the radboud faces database. Cognition and Emotion, 24(8), 1377–1388.

    Article  Google Scholar 

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

  21. Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., et al. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In CVPR.

  22. Li, C., & Wand, M. (2016). Precomputed real-time texture synthesis with Markovian generative adversarial networks. In ECCV.

  23. Li, M., Zuo, W., & Zhang, D. (2016) Deep identity-aware transfer of facial attributes. arXiv preprint arXiv:1610.05586

  24. Liu, M. Y., Breuel, T., & Kautz, J. (2017). Unsupervised image-to-image translation networks. In NIPS.

  25. Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep learning face attributes in the wild. In ICCV.

  26. Mathieu, M., Couprie, C., & LeCun, Y. (2016). Deep multi-scale video prediction beyond mean square error. In ICLR.

  27. Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784

  28. Nagano, K., Seo, J., Xing, J., Wei, L., Li, Z., Saito, S., et al. (2018). paGAN: Real-time avatars using dynamic textures. ACM Transactions on Graphics, 37(6), 258:1–258:12.

    Article  Google Scholar 

  29. Nam, S., Ma, C., Chai, M., Brendel, W., Xu, N., & Joo Kim, S. (2019). End-to-end time-lapse video synthesis from a single outdoor image. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1409–1418).

  30. Odena, A., Olah, C., & Shlens, J. (2017). Conditional image synthesis with auxiliary classifier GANs. In ICML.

  31. Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016). Context encoders: Feature learning by inpainting. In CVPR.

  32. Perarnau, G., van de Weijer, J., Raducanu, B., & Álvarez, J. M. (2016). Invertible conditional GANs for image editing. arXiv preprint arXiv:1611.06355

  33. Pumarola, A., Agudo, A., Martinez, A. A., Sanfeliu, A., & Moreno-Noguer, F. (2018). Ganimation: Anatomically-aware facial animation from a single image. In ECCV.

  34. Pumarola, A., Agudo, A., Sanfeliu, A., & Moreno-Noguer, F. (2018) Unsupervised person image synthesis in arbitrary poses. In CVPR.

  35. Radford, A., Metz, L., & Chintala, S. (2016). Unpaired image-to-image translation using cycle-consistent adversarial networks. In ICLR.

  36. Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee., H. (2016). Generative adversarial text to image synthesis. In ICML.

  37. Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. In Advances in neural information processing systems.

  38. Scherer, K. R. (1982). Emotion as a process: Function, origin and regulation. Social Science Information, 21, 555–570.

    Article  Google Scholar 

  39. Shen, W., & Liu, R. (2017). Learning residual images for face attribute manipulation. In CVPR.

  40. Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., & Webb, R. (2017). Learning from simulated and unsupervised images through adversarial training. In CVPR.

  41. Song, Y., Zhu, J., Wang, X., & Qi, H. (2018). Talking face generation by conditional recurrent adversarial network. arXiv preprint arXiv:1804.04786

  42. Susskind, J. M., Hinton, G. E., Movellan, J. R., & Anderson, A. K. (2008). Generating facial expressions with deep belief nets. In Affective computing. IntechOpen.

  43. Suwajanakorn, S., Seitz, S. M., & Kemelmacher-Shlizerman, I. (2017). Synthesizing obama: Learning lip sync from audio. ACM Transactions on Graphics, 36(4), 95.

    Article  Google Scholar 

  44. Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., & Nießner, M. (2016). Face2face: Real-time face capture and reenactment of RGB videos. In CVPR.

  45. Tulyakov, S., Liu, M. Y., Yang, X., & Kautz, J. (2018). Mocogan: Decomposing motion and content for video generation. In CVPR.

  46. Vondrick, C., Pirsiavash, H., & Torralba, A. (2016). Generating videos with scene dynamics. In Advances in neural information processing systems.

  47. Vougioukas, K., Petridis, S., & Pantic, M. (2018). End-to-end speech-driven facial animation with temporal gans. In BMVC.

  48. Wang, X., & Gupta, A. (2016). Generative image modeling using style and structure adversarial networks. In ECCV.

  49. Wang, Z., Liu, D., Yang, J., Han, W., & Huang, T. (2015). Deep networks for image super-resolution with sparse prior. In ICCV.

  50. Yu, H., Garrod, O. G., & Schyns, P. G. (2012). Perception-driven facial expression synthesis. Computers and Graphics, 36(3), 152–162.

    Article  Google Scholar 

  51. Zafeiriou, S., Trigeorgis, G., Chrysos, G., Deng, J., & Shen, J. (2017). The menpo facial landmark localisation challenge: A step towards the solution. In CVPRW.

  52. Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., & Metaxas, D. (2017). Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In ICCV.

  53. Zhou, H., Liu, Y., Liu, Z., Luo, P., & Wang, X. (2019). Talking face generation by adversarially disentangled audio–visual representation. In AAAI.

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

  55. Zhu, S., Fidler, S., Urtasun, R., Lin, D., & Loy, C. C. (2017b). Be your own prada: Fashion synthesis with structural coherence. In ICCV.

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This work is partially supported by an Amazon Research Award, by the Spanish Ministry of Economy and Competitiveness under Projects HuMoUR TIN2017-90086-R, ColRobTransp DPI2016-78957 and María de Maeztu Seal of Excellence MDM-2016-0656; by the EU Project AEROARMS ICT-2014-1-644271; and by the Grant R01-DC- 014498 of the National Institute of Health. We also thank Nvidia for hardware donation under the GPU Grant Program.

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Correspondence to Albert Pumarola.

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Pumarola, A., Agudo, A., Martinez, A.M. et al. GANimation: One-Shot Anatomically Consistent Facial Animation. Int J Comput Vis 128, 698–713 (2020).

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  • GAN
  • Face animation
  • Action-unit condition