Abstract
Facial expression manipulation aims at editing facial expression with a given condition. Previous methods edit an input image under the guidance of a discrete emotion label or absolute condition (e.g., facial action units) to possess the desired expression. However, these methods either suffer from changing condition-irrelevant regions or are inefficient for fine-grained editing. In this study, we take these two objectives into consideration and propose a novel method. First, we replace continuous absolute condition with relative condition, specifically, relative action units. With relative action units, the generator learns to only transform regions of interest which are specified by non-zero-valued relative AUs. Second, our generator is built on U-Net but strengthened by multi-scale feature fusion (MSF) mechanism for high-quality expression editing purposes. Extensive experiments on both quantitative and qualitative evaluation demonstrate the improvements of our proposed approach compared to the state-of-the-art expression editing methods. Code is available at https://github.com/junleen/Expression-manipulator.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning, pp. 214–223 (2017)
Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L.P.: Openface 2.0: facial behavior analysis toolkit. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition, FG 2018, pp. 59–66. IEEE (2018)
Barratt, S., Sharma, R.: A note on the inception score. arXiv preprint arXiv:1801.01973 (2018)
Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)
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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8789–8797 (2018)
Chu, M., Xie, Y., Leal-Taixé, L., Thuerey, N.: Temporally coherent GANs for video super-resolution (TecoGAN). arXiv preprint arXiv:1811.09393 (2018)
Friesen, E., Ekman, P.: Facial action coding system: a technique for the measurement of facial movement. Consulting Psychologists Press, Palo Alto (1978)
Geng, Z., Cao, C., Tulyakov, S.: 3D guided fine-grained face manipulation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9821–9830 (2019)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GAN. In: Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)
He, Z., Zuo, W., Kan, M., Shan, S., Chen, X.: AttGAN: facial attribute editing by only changing what you want. IEEE Trans. Image Process. 28, 5464–5478 (2019)
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)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv: Learning (2014)
Li, M., Zuo, W., Zhang, D.: Deep identity-aware transfer of facial attributes. arXiv preprint arXiv:1610.05586 (2016)
Liu, M., et al.: STGAN: a unified selective transfer network for arbitrary image attribute editing, pp. 3673–3682 (2019)
Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems, pp. 700–708 (2017)
Liu, M.Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems, pp. 469–477 (2016)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. Computer Science, pp. 2672–2680 (2014)
Mollahosseini, A., Hasani, B., Mahoor, M.H.: AffectNet: a database for facial expression, valence, and arousal computing in the wild. IEEE Trans. Affect. Comput. 10(1), 18–31 (2017)
Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2337–2346 (2019)
Paszke, A., et al.: Automatic differentiation in PyTorch (2017)
Peng, X.B., Kanazawa, A., Toyer, S., Abbeel, P., Levine, S.: Variational discriminator bottleneck: improving imitation learning, inverse RL, and GANs by constraining information flow. arXiv preprint arXiv:1810.00821 (2018)
Perarnau, G., Van De Weijer, J., Raducanu, B., Álvarez, J.M.: Invertible conditional GANs for image editing. arXiv preprint arXiv:1611.06355 (2016)
Pumarola, A., Agudo, A., Martinez, A.M., et al.: GANimation: one-shot anatomically consistent facial animation. Int. J. Comput. Vis. 128, 698–713 (2020). https://doi.org/10.1007/s11263-019-01210-3
Qiao, F., Yao, N., Jiao, Z., Li, Z., Chen, H., Wang, H.: Geometry-contrastive GAN for facial expression transfer. arXiv preprint arXiv:1802.01822 (2018)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, pp. 2234–2242 (2016)
Song, L., Lu, Z., He, R., Sun, Z., Tan, T.: Geometry guided adversarial facial expression synthesis. In: 2018 ACM Multimedia Conference on Multimedia Conference, pp. 627–635. ACM (2018)
Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: CVPR (2019)
Tulyakov, S., Liu, M.Y., Yang, X., Kautz, J.: MoCoGAN: decomposing motion and content for video generation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1526–1535 (2018)
Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_5
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wu, P.W., Lin, Y.J., Chang, C.H., Chang, E.Y., Liao, S.W.: RelGAN: Multi-domain image-to-image translation via relative attributes. arXiv preprint arXiv:1908.07269 (2019)
Zhang, G., Kan, M., Shan, S., Chen, X.: Generative adversarial network with spatial attention for face attribute editing. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_26
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)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Acknowledgements
This work was supported by NSFC (61671296, U1611461), National Key R&D Project of China (2019YFB1802701), MoE-China Mobile Research Fund Project (MCM20180702) and the Shanghai Key Laboratory of Digital Media Processing and Transmissions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ling, J., Xue, H., Song, L., Yang, S., Xie, R., Gu, X. (2020). Toward Fine-Grained Facial Expression Manipulation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12373. Springer, Cham. https://doi.org/10.1007/978-3-030-58604-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-58604-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58603-4
Online ISBN: 978-3-030-58604-1
eBook Packages: Computer ScienceComputer Science (R0)