Built on

Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13
2004
Earlier work this paper cites.
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a Deep Convolutional Network for Image Super-Resolution (2014)
2014
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014), http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
2014
Earlier work this paper cites.
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. CoRR abs/1411.7766
Original
2014
Earlier work this paper cites.
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR abs/1511.06434
Original
2015
Earlier work this paper cites.
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: CVPR (June 2015)
2015
Earlier work this paper cites.
Wu, X., He, R., Sun, Z.: A lightened CNN for deep face representation. CoRR abs/1511.02683
Original
2015
Earlier work this paper cites.
Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. CoRR abs/1608.00367
Original
2016
Earlier work this paper cites.

Similar

Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. CoRR abs/1611.07004
Original
2016
Cited alongside, same era.
Johnson, J., Alahi, A., Li, F.: Perceptual losses for real-time style transfer and super-resolution. CoRR abs/1603.08155
Original
2016
Cited alongside, same era.
Kim, J., Lee, J.K., Lee, K.M.: Deeply-recursive convolutional network for image super-resolution. In: CVPR. pp. 1637–1645 (2016)
2016
Cited alongside, same era.
Ledig, C., Theis, L., Huszar, F., Caballero, J., Aitken, A.P., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. CoRR abs/1609.04802
Original
2016
Cited alongside, same era.
Perarnau, G., van de Weijer, J., Raducanu, B., Álvarez, J.M.: Invertible Conditional GANs for image editing. In: NIPS Workshop on Adversarial Training (2016)

Then

Kim, T., Cha, M., Kim, H., Lee, J., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks. ICML (2017)
2017
Closest in time.
Liao, J., Yao, Y., Yuan, L., Hua, G., Kang, S.B.: Visual Attribute Transfer through Deep Image Analogy. SIGGRAPH (2017)
2017
Closest in time.
Yi, Z., Zhang, H., Gong, P.T., et al.: Dualgan: Unsupervised dual learning for image-to-image translation. arXiv preprint arXiv:1704.02510 (2017)
Original
2017
Closest in time.
Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., Metaxas, D.: Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: IEEE Int. Conf. Comput. Vision (ICCV). pp. 5907–5915 (2017)
2017
Closest in time.
Zhao, B., Wu, X., Cheng, Z.Q., Liu, H., Feng, J.: Multi-View Image Generation from a Single-View. ArXiv e-prints (Apr 2017)

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2017

Attribute-Guided Face Generation Using Conditional CycleGAN

Lu, Yongyi, Tai, Yu-Wing, Tang, Chi-Keung

Understand

We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes.

To address this problem, we condition the CycleGAN and propose conditional CycleGAN, which is designed to 1) handle unpaired training data because the training low/high-res and high-res attribute images may not necessarily align with each other, and to 2) allow easy control of the appearance of the generated face via the input attributes.
We demonstrate impressive results on the attribute-guided conditional CycleGAN, which can synthesize realistic face images with appearance easily controlled by user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses).
Using the attribute image as identity to produce the corresponding conditional vector and by incorporating a face verification network, the attribute-guided network becomes the identity-guided conditional CycleGAN which produces impressive and interesting results on identity transfer.

Built on

Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13
2004
Earlier work this paper cites.
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a Deep Convolutional Network for Image Super-Resolution (2014)
2014
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014), http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
2014
Earlier work this paper cites.
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. CoRR abs/1411.7766
Original
2014
Earlier work this paper cites.
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR abs/1511.06434
Original
2015
Earlier work this paper cites.
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: CVPR (June 2015)
2015
Earlier work this paper cites.
Wu, X., He, R., Sun, Z.: A lightened CNN for deep face representation. CoRR abs/1511.02683
Original
2015
Earlier work this paper cites.
Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. CoRR abs/1608.00367
Original
2016
Earlier work this paper cites.

Similar

Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. CoRR abs/1611.07004
Original
2016
Cited alongside, same era.
Johnson, J., Alahi, A., Li, F.: Perceptual losses for real-time style transfer and super-resolution. CoRR abs/1603.08155
Original
2016
Cited alongside, same era.
Kim, J., Lee, J.K., Lee, K.M.: Deeply-recursive convolutional network for image super-resolution. In: CVPR. pp. 1637–1645 (2016)
2016
Cited alongside, same era.
Ledig, C., Theis, L., Huszar, F., Caballero, J., Aitken, A.P., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. CoRR abs/1609.04802
Original
2016
Cited alongside, same era.
Perarnau, G., van de Weijer, J., Raducanu, B., Álvarez, J.M.: Invertible Conditional GANs for image editing. In: NIPS Workshop on Adversarial Training (2016)

Then

Kim, T., Cha, M., Kim, H., Lee, J., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks. ICML (2017)
2017
Closest in time.
Liao, J., Yao, Y., Yuan, L., Hua, G., Kang, S.B.: Visual Attribute Transfer through Deep Image Analogy. SIGGRAPH (2017)
2017
Closest in time.
Yi, Z., Zhang, H., Gong, P.T., et al.: Dualgan: Unsupervised dual learning for image-to-image translation. arXiv preprint arXiv:1704.02510 (2017)
Original
2017
Closest in time.
Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., Metaxas, D.: Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: IEEE Int. Conf. Comput. Vision (ICCV). pp. 5907–5915 (2017)
2017
Closest in time.
Zhao, B., Wu, X., Cheng, Z.Q., Liu, H., Feng, J.: Multi-View Image Generation from a Single-View. ArXiv e-prints (Apr 2017)

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2016

Cited alongside, same era.

Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: CVPR. pp. 1874–1883 (2016)

2016

Cited alongside, same era.

Wang, Y., Wang, L., Wang, H., Li, P.: End-to-end image super-resolution via deep and shallow convolutional networks. CoRR abs/1607.07680

Original

2016

Cited alongside, same era.

Huang, R., Zhang, S., Li, T., He, R.: Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis. ArXiv e-prints (Apr 2017)

2017

Cited alongside, same era.

2017

Closest in time.

Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Computer Vision (ICCV), 2017 IEEE International Conference on (2017)

Original

2017

Closest in time.

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) (June 2018)

2018

Closest in time.