Abstract
The goal of face attribute editing is altering a facial image according to given target attributes such as hair color, mustache, gender, etc. It belongs to the image-to-image domain transfer problem with a set of attributes considered as a distinctive domain. There have been some works in multi-domain transfer problem focusing on facial attribute editing employing Generative Adversarial Network (GAN). These methods have reported some successes but they also result in unintended changes in facial regions - meaning the generator alters regions unrelated to the specified attributes. To address this unintended altering problem, we propose a novel GAN model which is designed to edit only the parts of a face pertinent to the target attributes by the concept of Complementary Attention Feature (CAFE). CAFE identifies the facial regions to be transformed by considering both target attributes as well as “complementary attributes”, which we define as those attributes absent in the input facial image. In addition, we introduce a complementary feature matching to help in training the generator for utilizing the spatial information of attributes. Effectiveness of the proposed method is demonstrated by analysis and comparison study with state-of-the-art methods.
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References
Ak, K.E., Lim, J.H., Tham, J.Y., Kassim, A.A.: Attribute manipulation generative adversarial networks for fashion images. In: The IEEE International Conference on Computer Vision (ICCV) (2019)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv:1701.07875 (2017)
Chang, H., Lu, J., Yu, F., Finkelstein, A.: PairedCycleGAN: asymmetric style transfer for applying and removing makeup. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Chen, Y.C., et al.: Facelet-bank for fast portrait manipulation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Chen, Y.C., Shen, X., Lin, Z., Lu, X., Pao, I., Jia, J., et al.: Semantic component decomposition for face attribute manipulation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Fukui, H., Hirakawa, T., Yamashita, T., Fujiyoshi, H.: Attention branch network: learning of attention mechanism for visual explanation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (NeurIPS) (2014)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems (NeurIPS) (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. (TIP) (2017)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation (2017)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Kim, T., Kim, B., Cha, M., Kim, J.: Unsupervised visual attribute transfer with reconfigurable generative adversarial networks (2017)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv:1412.6980 (2014)
Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Lample, G., Zeghidour, N., Usunier, N., Bordes, A., Denoyer, L., Ranzato, M.: Fader networks: manipulating images by sliding attributes. In: Advances in Neural Information Processing Systems (NeurIPS) (2017)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Li, M., Zuo, W., Zhang, D.: Deep identity-aware transfer of facial attributes (2016)
Lin, M., Chen, Q., Yan, S.: Network in network. In: The International Conference on Learning Representations (ICLR) (2014)
Liu, M., et al.: STGAN: a unified selective transfer network for arbitrary image attribute editing. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems (NeurIPS) (2017)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Smolley, S.P.: Least squares generative adversarial networks. In: The IEEE International Conference on Computer Vision (ICCV) (2017)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv:1411.1784 (2014)
Park, J., Han, D.K., Ko, H.: Fusion of heterogeneous adversarial networks for single image dehazing. IEEE Trans. Image Process. (TIP) 29, 4721–4732 (2020)
Perarnau, G., Van De Weijer, J., Raducanu, B., Álvarez, J.M.: Invertible conditional GANs for image editing. arXiv:1611.06355 (2016)
Pumarola, A., Agudo, A., Martinez, A.M., Sanfeliu, A., Moreno-Noguer, F.: GANimation: anatomically-aware facial animation from a single image. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part X. LNCS, vol. 11214, pp. 835–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_50
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 (2015)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: The IEEE International Conference on Computer Vision (ICCV) (2017)
Shen, W., Liu, R.: Learning residual images for face attribute manipulation (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
Smilkov, D., Thorat, N., Kim, B., Viégas, F., Wattenberg, M.: SmoothGrad: removing noise by adding noise (2017)
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: The all convolutional net (2014)
Upchurch, P., et al.: Deep feature interpolation for image content changes. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
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. In: The IEEE International Conference on Computer Vision (ICCV) (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, Part VI. LNCS, vol. 11210, pp. 422–437. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_26
Zhao, B., Chang, B., Jie, Z., Sigal, L.: Modular generative adversarial networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 157–173. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_10
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: The IEEE International Conference on Computer Vision (ICCV) (2017)
Acknowledgment
Authors (Jeong gi Kwak and Hanseok Ko) of Korea University are supported by a National Research Foundation (NRF) grant funded by the MSIP of Korea (number 2019R1A2C2009480). David Han’s contribution is supported by the US Army Research Laboratory.
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Kwak, Jg., Han, D.K., Ko, H. (2020). CAFE-GAN: Arbitrary Face Attribute Editing with Complementary Attention Feature. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12359. Springer, Cham. https://doi.org/10.1007/978-3-030-58568-6_31
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