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Our goal with this survey is to provide an overview of the state of the art deep learning methods for face generation and editing using StyleGAN.
J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah, “Signature verification using a ”siamese” time delay neural network,” in Proceedings of the 6th International Conference on Neural Information Processing Systems , ser. NIPS’93. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1993, p. 737–744
1993
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Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing , vol. 13, no. 4, pp. 600–612, 2004
2004
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D. E. King, “Dlib-ml: A machine learning toolkit,” Journal of Machine Learning Research , vol. 10, pp. 1755–1758, 2009
2009
Earlier work this paper cites.
S. Garfield, “Official portrait of president-elect barack obama,” 2009, published under CC BY 2.0
2009
Earlier work this paper cites.
J. Parvizi, C. Jacques, B. L. Foster, N. Withoft, V. Rangarajan, K. S. Weiner, and K. Grill-Spector, “Electrical stimulation of human fusiform face-selective regions distorts face perception,” Journal of Neuroscience , vol. 32, no. 43, pp. 14 915–14 920, 2012
2012
Earlier work this paper cites.
X. Qin, X. Tan, and S. Chen, “Tri-subject kinship verification: Understanding the core of a family,” IEEE Transactions on Multimedia , vol. 17, no. 10, pp. 1855–1867, 2015
2015
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 3730–3738
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 2015
2015
Earlier work this paper cites.
A. Dosovitskiy and T. Brox, “Generating images with perceptual similarity metrics based on deep networks,” in Advances in Neural Information Processing Systems , D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, Eds., vol. 29, 2016
2016
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2818–2826
2016
Earlier work this paper cites.
V. S. Photography, “Actors headshots female white late twenties,” 2016, published under CC BY 2.0
2016
Earlier work this paper cites.
J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European conference on computer vision . Springer, 2016, pp. 694–711
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
J. Thies, M. Zollhofer, M. Stamminger, C. Theobalt, and M. Nießner, “Face2face: Real-time face capture and reenactment of rgb videos,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2387–2395
2016
Earlier work this paper cites.
A. Melnik, P. Legkov, K. Izdebski, S. M. Kärcher, W. D. Hairston, D. P. Ferris, and P. König, “Systems, subjects, sessions: to what extent do these factors influence eeg data?” Frontiers in human neuroscience , vol. 11, p. 150, 2017
2017
Earlier work this paper cites.
X. Huang and S. Belongie, “Arbitrary style transfer in real-time with adaptive instance normalization,” in 2017 IEEE International Conference on Computer Vision (ICCV) , 2017, pp. 1510–1519
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in International conference on machine learning . PMLR, 2017, pp. 214–223
2017
Earlier work this paper cites.
J. H. Lim and J. C. Ye, “Geometric gan,” arXiv preprint arXiv:1705.02894 , 2017
2017
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Communications of the ACM , vol. 60, no. 6, pp. 84–90, 2017
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Y. Zhang, L. Zheng, and V. L. L. Thing, “Automated face swapping and its detection,” in 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP) , 2017, pp. 15–19
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. R. A. Moniz, C. Beckham, S. Rajotte, S. Honari, and C. Pal, “Unsupervised depth estimation, 3d face rotation and replacement,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in International Conference on Learning Representations , 2018
2018
Earlier work this paper cites.
Y. Blau and T. Michaeli, “The perception-distortion tradeoff,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 6228–6237
2018
Earlier work this paper cites.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 6 2018
2018
Earlier work this paper cites.
S. Tulyakov, M.-Y. Liu, X. Yang, and J. Kautz, “Mocogan: Decomposing motion and content for video generation,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018
2018
Earlier work this paper cites.
D. Kononenko, Y. Ganin, D. Sungatullina, and V. Lempitsky, “Photorealistic monocular gaze redirection using machine learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 40, no. 11, pp. 2696–2710, 2018
2018
Earlier work this paper cites.
K. Vougioukas, S. Petridis, and M. Pantic, “End-to-end speech-driven facial animation with temporal gans,” in BMVC , 2018
2018
Earlier work this paper cites.
T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, G. Liu, A. Tao, J. Kautz, and B. Catanzaro, “Video-to-video synthesis,” in NeurIPS , 2018
2018
Earlier work this paper cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in IEEE/CVF conference on computer vision and pattern recognition , 2019
2019
Earlier work this paper cites.
M. Zhang, N. Wang, Y. Li, and X. Gao, “Neural probabilistic graphical model for face sketch synthesis,” IEEE Transactions on Neural Networks and Learning Systems , vol. 31, no. 7, pp. 2623–2637, Jul. 2020. [Online]. Available: https://doi.org/10.1109/tnnls.2019.2933590
2019
Earlier work this paper cites.
M. Zhang, N. Wang, Y. Li, and Gao, “Deep latent low-rank representation for face sketch synthesis,” IEEE Transactions on Neural Networks and Learning Systems , vol. 30, no. 10, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4690–4699
2019
Earlier work this paper cites.
Y. Song, J. Zhu, D. Li, A. Wang, and H. Qi, “Talking face generation by conditional recurrent adversarial network,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence , 2019, pp. 919–925
2019
Earlier work this paper cites.
L. Yu, J. Yu, and Q. Ling, “Mining audio, text and visual information for talking face generation,” in 2019 IEEE International Conference on Data Mining (ICDM) , 2019, pp. 787–795
2019
Earlier work this paper cites.
L. Tran, X. Yin, and X. Liu, “Representation learning by rotating your faces,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 41, pp. 3007–3021, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 8110–8119
2020
Earlier work this paper cites.
T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila, “Training generative adversarial networks with limited data,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Earlier work this paper cites.
R. Abdal, Y. Qin, and P. Wonka, “Image2stylegan++: How to edit the embedded images?” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 8296–8305
2020
Earlier work this paper cites.
A. Tewari, M. Elgharib, G. Bharaj, F. Bernard, H.-P. Seidel, P. Pérez, M. Zollhofer, and C. Theobalt, “Stylerig: Rigging stylegan for 3d control over portrait images,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020
2020
Cited alongside, same era.
M. Zhang, N. Wang, Y. Li, and X. Gao, “Bionic face sketch generator,” IEEE Transactions on Cybernetics , vol. 50, no. 6, 2020
2020
Cited alongside, same era.
L. Verdoliva, “Media forensics and deepfakes: An overview,” IEEE Journal of Selected Topics in Signal Processing , vol. 14, pp. 910–932, 2020
2020
Cited alongside, same era.
E. Burkov, I. Pasechnik, A. Grigorev, and V. Lempitsky, “Neural head reenactment with latent pose descriptors,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 13 783–13 792
2020
Cited alongside, same era.
S. Zhou, K. C. Chan, C. Li, and C. C. Loy, “Towards robust blind face restoration with codebook lookup transformer,” in NeurIPS , 2022
2022
Closest in time.
Y. Gu, X. Wang, L. Xie, C. Dong, G. Li, Y. Shan, and M.-M. Cheng, “Vqfr: Blind face restoration with vector-quantized dictionary and parallel decoder,” in ECCV , 2022
2022
Closest in time.
“Lip-syncing thanks to artificial intelligence,” https://www.mpg.de/12226519/synchronisation-facial-expressions-video , accessed: 2022-10-17
2022
Closest in time.
Y. Liu and J. Chen, “Unsupervised face frontalization using disentangled representation-learning cyclegan,” Computer Vision and Image Understanding , vol. 222, 2022
2022
Closest in time.
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M. Li, J. Lin, Y. Ding, Z. Liu, J.-Y. Zhu, and S. Han, “Gan compression: Efficient architectures for interactive conditional gans,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020
2020
Cited alongside, same era.
Y. Viazovetskyi, V. Ivashkin, and E. Kashin, “Stylegan2 distillation for feed-forward image manipulation,” in European conference on computer vision . Springer, 2020, pp. 170–186
2020
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
J. Zhu, Y. Shen, D. Zhao, and B. Zhou, “In-domain gan inversion for real image editing,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVII , 2020, pp. 592–608
2020
Cited alongside, same era.
Y. Shen, J. Gu, X. Tang, and B. Zhou, “Interpreting the latent space of gans for semantic face editing,” in CVPR , 2020
2020
Cited alongside, same era.
E. Härkönen, A. Hertzmann, J. Lehtinen, and S. Paris, “Ganspace: Discovering interpretable gan controls,” Advances in Neural Information Processing Systems , vol. 33, pp. 9841–9850, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2022
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2022
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R. Or-El, X. Luo, M. Shan, E. Shechtman, J. J. Park, and I. Kemelmacher-Shlizerman, “Stylesdf: High-resolution 3d-consistent image and geometry generation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022
2022
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2022
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A. Sauer, K. Schwarz, and A. Geiger, “Stylegan-xl: Scaling stylegan to large diverse datasets,” ACM SIGGRAPH 2022 Conference Proceedings , 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:246441861
2022
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2022
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2022
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E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. De Mello, O. Gallo, L. J. Guibas, J. Tremblay, S. Khamis et al. , “Efficient geometry-aware 3d generative adversarial networks,” in IEEE/CVF Conf. on Computer Vision and Pattern Recognition , 2022
2022
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S. Yang, L. Jiang, Z. Liu, , and C. C. Loy, “Vtoonify: Controllable high-resolution portrait video style transfer,” ACM Transactions on Graphics (TOG) , vol. 41, no. 6, pp. 1–15, 2022
2022
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“Facial features report,” https://www.cryobank.com/_resources/pdf/sampleinformation/facialfeaturesample.pdf , accessed: 2023-09-27
2023
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H. Li, X. Hou, Z. Huang, and L. Shen, “Stylegene: Crossover and mutation of region-level facial genes for kinship face synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 20 960–20 969
2023
Closest in time.
Y. Poirier-Ginter and J.-F. Lalonde, “Robust unsupervised stylegan image restoration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2023, pp. 22 292–22 301
2023
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“How deepfake technology can change the movie industry,” https://screenrant.com/movies-deepfake-technology-change-hollywood-how/ , accessed: 2023-09-27
2023
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2023
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2023
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2023
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2023
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2023
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Z. Huang, K. C. Chan, Y. Jiang, and Z. Liu, “Collaborative diffusion for multi-modal face generation and editing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 6080–6090
2023
Closest in time.
N. G. Nair, W. G. C. Bandara, and V. M. Patel, “Unite and conquer: Plug & play multi-modal synthesis using diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 6070–6079
2023
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R. Zhang, J. Han, A. Zhou, X. Hu, S. Yan, P. Lu, H. Li, P. Gao, and Y. Qiao, “Llama-adapter: Efficient fine-tuning of language models with zero-init attention,” arXiv preprint:2303.16199 , 2023
2023
Closest in time.
A. Sevastopolsky, Y. Malkov, N. Durasov, L. Verdoliva, and M. Nießner, “How to boost face recognition with stylegan?” International Conference on Computer Vision (ICCV) , 2023
2023
Closest in time.
M. Wu, H. Zhu, L. Huang, Y. Zhuang, Y. Lu, and X. Cao, “High-fidelity 3d face generation from natural language descriptions,” in Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
Closest in time.
X. Pan, A. Tewari, T. Leimkühler, L. Liu, A. Meka, and C. Theobalt, “Drag your gan: Interactive point-based manipulation on the generative image manifold,” in ACM SIGGRAPH 2023 Conference Proceedings , 2023, pp. 1–11
2023
Closest in time.
H. Jia, Q. Wang, O. Tov, Y. Zhao, F. Deng, L. Wang, C.-L. Chang, T. Hou, and M. Grundmann, “Blazestylegan: A real-time on-device stylegan,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4689–4693
2023
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2023
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2023
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M. Kim, F. Liu, A. Jain, and X. Liu, “Dcface: Synthetic face generation with dual condition diffusion model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 12 715–12 725
2023
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2023
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2023
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Z. Wang, Z. Zhang, X. Zhang, H. Zheng, M. Zhou, Y. Zhang, and Y. Wang, “Dr2: Diffusion-based robust degradation remover for blind face restoration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1704–1713
2023
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G. Kim, H. Shim, H. Kim, Y. Choi, J. Kim, and E. Yang, “Diffusion video autoencoders: Toward temporally consistent face video editing via disentangled video encoding,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023
2023
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O. Avrahami, O. Fried, and D. Lischinski, “Blended latent diffusion,” ACM Transactions on Graphics (TOG) , vol. 42, no. 4, pp. 1–11, 2023
2023
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A. Sauer, T. Karras, S. Laine, A. Geiger, and T. Aila, “Stylegan-t: Unlocking the power of gans for fast large-scale text-to-image synthesis,” arXiv preprint:2301.09515 , 2023
2023
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M. Kang, J.-Y. Zhu, R. Zhang, J. Park, E. Shechtman, S. Paris, and T. Park, “Scaling up gans for text-to-image synthesis,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
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M. Zheng, H. Zhang, H. Yang, and D. Huang, “Neuface: Realistic 3d neural face rendering from multi-view images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 16 868–16 877
2023
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R. Abdal, H.-Y. Lee, P. Zhu, M. Chai, A. Siarohin, P. Wonka, and S. Tulyakov, “3davatargan: Bridging domains for personalized editable avatars,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4552–4562
2023
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J. Sun, X. Wang, L. Wang, X. Li, Y. Zhang, H. Zhang, and Y. Liu, “Next3d: Generative neural texture rasterization for 3d-aware head avatars,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 20 991–21 002
2023
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Z. Ma, X. Zhu, G.-J. Qi, Z. Lei, and L. Zhang, “Otavatar: One-shot talking face avatar with controllable tri-plane rendering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 16 901–16 910
2023
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2023
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F. Yin, Y. Zhang, X. Wang, T. Wang, X. Li, Y. Gong, Y. Fan, X. Cun, Y. Shan, C. Oztireli et al. , “3d gan inversion with facial symmetry prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 342–351
2023
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S. Yang, L. Jiang, Z. Liu, and C. C. Loy, “Styleganex: Stylegan-based manipulation beyond cropped aligned faces,” in ICCV , 2023
2023
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